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When you want to buy something, you seldom directly opt for an unknown brand or a new shopping portal you’ve only heard of. This is because you are driven to trust the brands or e-commerce portals, or a particular offline store based on your previous experience. You feel connected because somehow you find items that align with your interests and preferences. 

In offline stores, one can say it largely depends on the relationship building of the owners and the shoppers. When it comes to online shopping, there is no face-to-face meeting. Yet, your favorite brands know what you like and recommend products you will likely purchase sooner or later. 

This is no magic. Say hello to recommendation systems, one of the most frequently used machine learning techniques. 

What are recommendation systems?

Recommendation systems are advanced data filtering systems powered by AI, recommending the most relevant item to a particular customer or user. It uses behavioral data and machine learning algorithms to predict the most relevant content or services for customers.

For instance, when you watch a web series or a movie on Netflix, you will see recommendations for shows and movies of similar genres. Netflix uses recommendation systems to analyze your watching preferences and browsing activities to suggest relevant content. 

Doing this helps reduce users’ time to browse through thousands of contents to find a new show or movie to watch.

Recommendation Systems: The Science Behind It

A recommendation system is an algorithm that uses big data to suggest relevant products and services to users. Since big data fuels recommendations, the inputs required for model training are crucial. 

It can work on various types of data depending on your business goals. For example, it can be based on user demographic data, past behavior, purchase habits, interactions with your product or website, and even search history. Recommendation systems analyze the data to identify patterns and then make suggestions for products most likely to appeal to the users.

For instance, on Amazon, you will discover related products on the homepage, upon adding items to your cart, reviewing a particular product, or completing a purchase. There is always a recommended list of items that align with your past purchase and overall browsing patterns. 

Recommendation systems can offer personalized experiences to customers by providing relevant recommendations on various topics. They also help businesses increase customer engagement and loyalty by recommending related items and services.

Why use Recommendation Systems?

An essential component of recommendation engine algorithms is filtering based on different aspects. The recommender function gathers information about the user and predicts the most relevant product. For example, they predict user ratings and preferences based on recurring actions.

Recommendation system models are crucial in helping us have a hassle-free and seamless user experience. It also helps expose more inventory or content that we might not discover otherwise amidst the enormous volumes of data. There are many uses for recommendation engines:

• Personalized content: Improves the on-site experience by creating dynamic recommendations for a diverse audience.
• Better search experience: Categorizes products based on their features.
• Increased sales: Recommends products to customers based on their purchase history, allowing them to make more informed decisions. 
• Re-engagement strategies: Helps bring users back by suggesting relevant items or services they may be interested in.
• Cross-selling and up-selling opportunities: Identifies complementary items that customers may have yet to consider or be aware of. 
• Collecting valuable customer data: Generates useful insights into user behavior and preferences. 
• Optimizing marketing campaigns: Analyzes customer data to refine targeted promotions and campaigns to drive higher conversion rates.

Product recommendations on online shopping apps, social media news feeds, and Google ads are examples of recommendation systems in our day-to-day life. 

Engineering Behind Recommendation Systems

Recommender Systems (RSs) are advanced software tools and techniques that recommend products to a user that they find useful. The recommendations involve different decision-making processes, like things to buy, music to listen to, or online news to read.

The four clearly defined, logical steps of a recommendation system are:

1. Data Collection

The first and most crucial stage in developing a recommendation engine is obtaining the appropriate data for each user. There are two kinds of data:

• Explicit data is information gathered from user inputs like product ratings, reviews, likes, and dislikes.
• Implicit data comprises details obtained from user behaviors such as web searches, clicks, cart actions, search logs, and order histories

Since each user’s data profile will change over time to become more distinctive, it is also essential to gather consumer characteristic information like

Demographics (age, gender)	User demographics refer to the various characteristics of individuals, such as age, gender, income, education level, location, and cultural background. In recommendation systems, user demographics can be used to personalize recommendations and improve user experience. In addition, it encourages engagement and loyalty to drive more business.
Psychographics (interests, value)	Psychometrics is the scientific study of human mental processes and behavior and can be used to develop more accurate user recommendations. By tailoring recommendations based on personality, interests, and behavior, companies can increase users’ likelihood of engaging with products or services.
Feature Information (genre, object type)	Feature information such as genre, object type, and keywords can also be used to personalize recommendations. By leveraging these characteristics, companies can create more targeted and relevant content for their users. For example, an online clothing retailer could use feature information to suggest items based on a user’s style preferences or the season.
Contextual Information (time of day, location)	Contextual information such as time of day, weather conditions, and location can be used to determine what content is most appropriate for each individual at any given moment. For instance, if a user is located in a city that experiences hot summers and cold winters may be prompted with seasonal items or activities suitable for the local climate.

2. Data Storage

Data storage is essential for any business as it allows them to store and access customer information. By monitoring user data, a business can gain insights into its target audience, including purchasing habits and preferences.

 There needs to be enough scalable storage as you gather more data. Depending on the data you gather, you have various storage options, including NoSQL, a regular SQL database, MongoDB, and AWS.

Considerations for selecting the best storage alternatives for Big Data include portability, integration, data storage space, and ease of implementation.

3. Data Analysis

Data analysis is required to provide prompt recommendations. Therefore, the data must be probed and examined. Data analysis techniques that are most frequently used include:

• Real-time analysis involves the system using tools to assess and analyze events as they are happening. Then, when we wish to make prompt recommendations, this strategy is typically used.
• Batch analysis is a method for routinely processing and evaluating data. When we wish to send emails with recommendations, this strategy is typically used. 
• A near-real-time analysis is practical when you do not immediately require the data. It allows you to process and analyze the data in minutes rather than seconds. The main application of the strategy is when we offer suggestions to users while they are still on the website.

4. Data Filtering

The data must be appropriately filtered after analysis to produce insightful recommendations. During filtration, Big Data is subjected to various matrices, mathematical algorithms, and rules for the best suggestion. The recommendations that result from this filtering depend on the algorithm you choose.

Recommendation engines rely on understanding relationships. Relationships give recommender systems a wealth of information and a thorough knowledge of their clients. Three different kinds typically exist: User to product: When specific users have a preference or affinity for particular goods they need, there is a user-product relationship User to user: Users with comparable preferences for a given good or service form user-user relationships Product to user: Product-product relationships develop when two products are similar, whether through description or appearance. These are then shared with an interested user

How do Recommendation Engines Gather Data?

Recommendation engines gather data based on the following:

• User Behavior: Information regarding user interaction with a product can be gleaned from user behavior data. Rating clicks and purchase history can all be used to gather it.
• User Demographics: Users’ data, such as their age, education, income, and location, are tied to their demographic data.
• Product Attributes: Information about a product’s attributes includes information on the product itself, such as the genre of a book, the movie actors, or the cuisine of a dish.

Challenges and Advantages of Using Recommendation Systems

Challenges: 

Implementing a recommendation system is rather complicated. The key challenges are: 

• Data Collection and Integration: The first challenge is collecting data from various sources, such as customer profiles, product catalogs, purchase histories, social media accounts, etc., and then integrating the data into a unified format for the recommendation engine. The process can be time-consuming and labor-intensive.
• Algorithms: Choosing the right algorithms for your recommendation engine is also challenging since you need to build an algorithm that understands user preferences and makes accurate predictions based on those preferences. Additionally, new algorithms must be implemented as customer preferences change over time.
• Accuracy: Once you have chosen the best algorithms for your purposes, it can still be difficult to ensure accuracy in the system’s output. This becomes even more difficult as the amount of data increases.
• Scalability: As your business grows, so does the volume of data, and it can be difficult to ensure that your recommendation engine can handle these large volumes of data without a performance decrease.
• Data Storage: Storing all of the necessary data requires considerable space and resources. Additionally, the data must be kept up-to-date to ensure the system’s output accuracy.
• Privacy and Security: Customers may be concerned about privacy when they share their personal information with a recommendation engine. It is important to have a strong security measure that protects customer data and prevents unauthorized access or use for malicious purposes.

Despite the steep implementation curve, companies continue to rely heavily on recommendation systems. This is because user recommendations have a very strong influence on user purchase decisions. The TWO major advantages of using a recommendation system will always outweigh all the challenges. 

Advantages: 

• Deliver Personalized and Relevant Content

Recommendation systems enable brands to customize the customer experience by identifying and suggesting items that may pique the customer’s interest. Additionally, the recommendation engine allows you to analyze the customer’s previous browsing history and current website usage. With the help of these real-time data, brands can deliver the most relevant product recommendation. 

For instance, Instagram often suggests pages or brands you might want to start following based on your preferences. Similarly, based on the kind of reels you watch, Instagram keeps suggesting new ones. This keeps the addiction on as you continue scrolling through new recommendations you can’t deny liking. It boosts engagement via personalized content recommendations. 

• Increase Sales and Average Order Value

You can significantly enhance your revenue and average order value (AOV) by bringing in more website visitors by adding recommended products.

A recommendation system enables users to drive more conversions and deliver a high level of relevance that will boost sales. It exposes the customers to more products they are more likely to purchase.

You can add multiple data sets to your recommendation algorithm using a recommendation engine. The data sets will help provide recommendations in real time.

For instance, when looking at a product on Amazon, you will always find two sections back-to-back – customers who viewed this have also viewed it, and customers who bought this also bought. These two sections will push you to consider different products that go well with the product you are trying to buy. 

Let’s say you are looking for a channel file on Amazon and have zeroed in on one of the options. As you scroll down to read more about its features and look at a comparative table (which Amazon always adds to propel better decision-making, ideally for the more priced product), you will come across two sections like this: 

Now, offline stores will call this push selling. This is exactly what Amazon is doing here. Recommending similar or related products often triggers human brains to explore those products and probably buy or add them to the cart. Towards the end of the purchase cycle, the order value automatically goes up from what it was when the cycle started. 

Filtering Algorithms Marketers Must Understand

To put a recommendation engine into proper functioning, marketers must also understand the algorithms that make up this system. Three major types of filtering algorithms are at play: Collaborative filtering content-based filtering Hybrid System

Collaborative Filtering

Collaborative filtering gathers information regarding customers’ activities or preferences to predict user interest. It is usually acknowledged based on their similarities with other users who might have the same preference. Similar customers are found using customer characteristics like demographics and psychographics. 

E-commerce platforms like Amazon and Myntra are pioneers in effectively implementing collaborative filtering. 

It works by gathering preferences from each user to determine a Customer X Product Matrix.

It is also known as the matrix factorization method and can be used to determine how similar user evaluations or interactions are. 

For example, according to the straightforward user-item matrix, Ted and Carol bought products B and C. Bob also searched for product B. Matrix factorization determines that users who enjoyed B also like C, making C a potential recommendation for Bob.

Collaborative filtering is divided as follows: 

User-item collaborative filtering	Like-minded customers are spotted based on similar rating patterns. It is a method for recommending products based on ratings from other users.
Item-item collaborative filtering	Similarities between multiple items are calculated. A recommendation approach that looks for comparable products based on the things consumers have already shown interest in or interact with.

Content-based Filtering

Predictions made by content-based engines are based on end-user interest in a particular product. The engine uses metadata to find and suggest related content items once a user interacts with a piece of content. 

You can spot this recommender system on news websites with prompts like “You may also be interested in reading” or “You may like.”

For instance, on Medium, you will always find recommendations for articles similar to the ones you are reading or have read. 

The recommendation engine algorithms filter content based on the following:

• User ratings: User ratings are of two types: Explicit and Implicit.  User profiles and star ratings are common sources of explicit data for recommendation systems (difficult to achieve). Implicit data is based on users’ engagement with the item. These are simple to obtain, including purchases, clicks, and views.
  
• Product similarity: The most effective method for recommending products based on how much a customer would like a certain item is product similarity. Users may be shown comparable products if they browse or look for a specific item. Here’s how Amazon does it.
  

• User similarity: It is a particular kind of algorithm for recommendation systems that suggest products based on product resemblances. Using data from previous user-product interactions, the algorithm generates suggestions. It assumes that individuals with similar interests will favor related goods. This is how Myntra does it: 

In Conclusion:

Note: The debate on privacy versus personalization has been hot since the recommendation systems rise. While such technologies have enabled organizations to provide tailor-made customer experiences through big data analytics, there is an inherent tension between these two concepts.

Companies must collect and store large amounts of data to generate personalized customer recommendations. Reports show that Big Data analytics has helped them increase revenue by 30%. 

On the other hand, companies must ensure that all collected data is securely stored and not misused. Survey results show 97% of consumers are concerned about their data being misused by companies and the government.

Ultimately, striking a balance between privacy and personalization should be the primary goal to ensure that customer data remains secure while providing an enjoyable customer experience. 

With this in mind, businesses can leverage big data-powered recommendation engines to offer a customized experience that will keep their customers returning for more.

Images from analytixlabs.co.in

The post The Engineering Behind Recommendation Systems appeared first on noupe.

Sentiments and emotions are hard to quantify, but, ironically, are the most crucial factors when a user makes a purchase decision. Users share their thoughts and opinions across various social platforms day-in-out. To make the most of it, brands need to monitor the chatter and derive useful insights that help understand user emotions, intent, and purpose. After all, every brand wants to know how their target users “feel” about them!

What is Social Sentiment Analysis? 

Marketers are obsessed with metrics, and why not. Metrics help you boost your business in the right direction. However, metrics cannot surpass your users’ sentiments and feelings. 

Social sentiment analysis can help brands dig deep into how their target users “feel” about them and turn social insights into actionable data. When a brand tries to incorporate user perceptions into its marketing and branding strategy, you get a successful and loved brand. 

Social sentiment analysis, or opinion mining, is a natural language processing technique that tells you if the data acquired is positive, negative, or neutral. This enables brands to create a better user experience. After all, it is the end-users experience that matters.

Sentiment Analysis + AI

AI tools are paving the way in every domain we can think of. Whether driving business goals, writing content, or generating customized audio, there is an AI tool for everything. 

It is only obvious that AI found its way into analyzing social sentiments. It pits together the power of two of its subfields – 

• Natural Language Processing
• Machine Learning

The technology behind sentiment analysis

Any AI-enabled sentiment analysis tool has these two subfields at its core. 

NLP converts human language into a language that machines understand. It implements syntactic (understand text structure) and semantic (identify meaning) techniques. 

Once the text is processed, machine learning algorithms come into play for classifications. Machine learning algorithms help identify patterns in data and make relevant predictions based on them. 

However, you must understand that machine learning algorithms do not rely on explicit instructions. Rather, it learns from its existing data set, i.e., the training data. 

In layman’s terms, a machine learning model will classify texts by sentiments based on the text emotions it is trained on. Each emotion must be labeled with corresponding categories and tags. 

After seeing a few examples, the model learns to associate a given text with a specific tag. Based on this, it starts predicting tags for unseen content. 

Sentiment analysis with AI lets you tag a huge volume of data sets simultaneously and in real-time. Since machine learning algorithms learn over time, your sentiment analysis models will only get smarter. 

Social Sentiment Analysis with AI

Social sentiment analysis tools follow the same model. It scouts social channels to identify and classify texts (social messages) into positive or negative. For instance, a social post stating, “I love using this tool” will give positive results in sentiment analysis.

Check out how sentiment analysis work. 

Featuring: MonkeyLearn’s Free Sentiment Analysis Tool. 

But, is it the same as social listening? 

Social listening vs. social sentiment analysis

While marketers use these two terms interchangeably, they are different. 

Social Listening	Social sentiment analysis
Social listening captures brand mentions across the web.  It is not limited to social channels. Brand mentions can be anywhere – blogs, social channels, forums, communities, etc.  It helps answer questions like –  Where do people talk about my brand? How do they perceive my brand? How does my brand perform against my competitors? Who can be my key influencers?	Social analytics provide actionable insights. It analyzes brand performance on social media at a deeper level and helps you understand the impact of your social efforts.  It helps answer questions like:  How effectively users are engaging on social media? How many people can see your brand? What are the user demographics and interests?

Social sentiment analysis can be tagged as a subset of social listening. Once you discover your brand mentions, the next step is to analyze the sentiment across those mentions. 

This allows brands to detect emergencies and take appropriate action before it is late. After all, more social mentions are not always equal to positive mentions. 

For instance, the eCommerce giant was in hot soup in 2019 for apparently hurting Hindu sentiments when it started showcasing products like doormats, slippers, and toilet seat covers with pictures of Hindu gods and goddesses. A Twitter user posted images of the same and was backed by thousands of people urging the brand to remove the entire catalog. 

The aggression was severe, to the point where users threatened to stop using the app or delete the app, altogether! And why not. It was the second time Amazon made the same mistake. 

As a result, Amazon clarified that the said product catalogs are removed and that all sellers will adhere to the selling guidelines. If sellers do not adhere to the selling guidelines, their products will be removed from the eCommerce portal. 

Owing up social backlash with utmost honesty and rectifying mistakes – brands can do it right if they successfully listen to their users and analyze the sentiment around the chatter. 

Types of Social Sentiment Analysis

Opinions (or sentiments) on social media are wide-ranged. They can be extremely positive or negative, somewhat positive or negative, or absolutely neutral. This is also termed as polarity-driven analysis. 

Here’s how you can grade emotions from the tone of the text. 

User opinion	Sentiment analysis
I am so stocked to see this feature live. Can’t wait to try it out	Extreme positive
Why would you remove the icon @xyz brand?	Somewhat negative
I am never coming back to this website ever, ever again. Pathetic customer service.	Extreme negative
Not sure if this feature will help marketers in the long run	Neutral
I love buying from @xyz but this time you’ve really goofed up	Somewhat negative

Sentiment analysis models

Grading sentiments as positive, negative, or neutral is the first level. To implement AI-enabled social sentiment analysis, you must go beyond polarity. 

Some advanced and popular types of sentiment analysis include: 

• Emotion analysis

Emotion-driven analysis helps you dig deeper into user emotions like anger, happiness, or frustration around your product/brand. In most cases, sentiment analysis tools use lexicons i.e. list of words that conveys emotions, or resort to advanced machine learning algorithms. 

The downside of using lexicons is that the correct emotion is often not captured. Lexicons contain words that specify a certain kind of emotion. For instance, words like bad or kill often resonates with anger. However, a social opinion stating this feature is killing it indicates happiness. 

For instance, look at this tweet with all the bad words, but the tweet’s emotion is positive and happy. In such cases, the emotion analysis can go wrong unless your sentiment analysis also considers the emojis. 

In that case, the algorithm will predict the emotion correctly. 

• Aspect-based analysis

Aspect-based analysis helps brands understand what exact feature or part of ad users tag as good, bad, or ugly!

For instance, This filter does not look good on nigh-mode pictures instantly tells a brand that the opinion is solely about the said filter. The brand can improve the filter or launch a new one for the camera’s night mode. Either way, aspect-based analysis helps dig deep into what users want and what they do not. 

• Multilingual sentiments

This involves a lot of pre-processing and resources. You will need lexicons (available online), noise, and translation algorithms (you must build them) to detect the sentiment. 

Multilingual sentiment analysis works for brands with users from varied ethnicities, cultures, and demographics. 

Social sentiment analysis to Understand Customers

There is no other alternative for brands but to listen to and understand their end-users. As more and more users become aware of the power of social media and brand positioning, brands must leverage social monitoring and sentiment analysis. 

Here’s how you can use sentiment analysis to your benefit. 

1. Spot opportunities to improve user experience

When you start monitoring brand mentions across social channels (and the web), you monitor compliments and complaints. This gives you a detailed insight into what your users expect from you. While compliments show that your product/services are built in the right direction, complaints show the gap that you might be missing otherwise. 

For instance, a complaint need not be aggressive or extremely negative. It can also be around a missing feature or service you have not yet introduced. Monitoring such mentions and engaging with users can help you identify what your users seek. If you find most users voting for that missing feature, you know it’s time to get it done. 

One brand that I absolutely love using is Canva, not only because the templates and design possibilities help marketers like me design pro images – but also because this brand listens and responds. 

Check this tweet from one of its users and how Canva handled it. 

There are many instances when you feel like a feature is missing and validate the same via social media. Social posts like these are also part of sentiment analysis. The score may not be as high as a proper negative review, but it will still be graded as negative. 

Pro-tip to handle such mentions: Own up to the gap and be empathetic. This lets the user know you’ve noted the issue and will work around it. It builds trust and also improves user experience. When a user knows he/she is heard, that’s all that matters to build a healthy brand reputation. 

2. Reduce customer wait time

Sentiment analysis tools will show you sentiments over time, like days, weeks, and months. Simultaneously, it will also tag social posts as positive, negative, and neutral for you to take quick action. 

For instance, I used a free sentiment analyzer tool to review the sentiment score for a random post. 

The tool analyzed the text and scored -100, indicating extremely negative and serious issues. The post uses a sarcastic tone to complain, but the tool accurately captures the negative sentiment. 

Similarly, I used one of the genuine tweets from Canva’s Twitter channel and ran an analysis on the same – 

While this tweet is regarding a missing feature in the product, the sentiment is still positive and generates a 100 score on the sentiment scorecard. It contains lexicons like ‘not sure’, which can easily get tagged as negative sentiments. However, looking at the context, the tool marks it as positive. 

In both cases, you must be quick to respond. 

• Responding to a positive or neutral social post will help you build better relationships with your users, along with instant gratification. 
• Responding to an issue or complaint on social media posts with either a solution or a process/resource to resolve the issue quickly can help you build user retention. 

No one wants to wait endlessly. Forget endless; users hate to wait beyond an hour. Research shows users expect a reply within 24 hours. Your users will start rethinking your brand within seconds of encountering an issue. So, the quicker you are, the better it is. 

For instance, Apple’s customer support is always prompt in its reply. While it does not fully resolve the issue immediately, the response time is less than a minute which is a breather for any users stuck with an issue. The response contains either some general steps to resolve the issue or a quick message to DM them the issue using the send us a private message CTA included in the response. [Tweet] 

What I love about this social handle is that every response (although these are automated) has customization. For example, the user complains about iMessage, so the response includes the ‘iMessage’ term to give it a personal touch. 

Pro-tip to reduce wait time: The best way to reduce wait time is by automating tas tagging for incoming support tickets. 

You can train your customer support team to use sentiment analysis to tag incoming tickets and automatically put negative tickets at the top of the queue. 

Simultaneously, you can create rules to automatically get negative tickets assigned to the most experienced representative to guarantee the best attention. 

3. Define Neutral and Emojis for accurate analysis

Defining positive and negative is easier than defining neutral. It is hard to train your machine-learning model to detect neutral tags. 

How you perceive neutral plays an important role in training your sentiment analysis model. Each tagging data will require tagging criteria so that a good definition can go a long way. Here’s how you can do this: 

1. Classify all objective texts as neutral, especially those that do not have explicit sentiments. 
2. If your data is still pre-processed, you can tag it neutral since the irrelevant data is yet to be filtered. But do it with caution. Sometimes, it may add more noise and hamper the performance. 
3. Texts like “I wish it could do XYZ” are usually neutral. However, you may find it harder to categorize wish texts like “I wish xyz did better than abc”. 

Next comes categorizing emojis. Emojis are of two types: 

• Western emojis with two characters
• Easter emojis with longer combinations of characters of vertical nature

Emojis play a pivotal role in determining the sentiment of the texts, especially in tweets. 

In this case, you must be careful of character and word levels when performing sentiment analysis. 

Pro Tip: Consider pre-processing social media content, transforming emojis into tokens, and whitelisting them. 

How to do social sentiment analysis [Use Case: Twitter]

Twitter offers instant gratification, which makes it the most accurate blueprint of user opinion. Monitoring brand mentions and competitor mentions can help in understanding the ‘emotion’ of users, how they feel about a product or a particular feature, and what can trigger them incorrectly. 

Twitter sentiment analysis can be done using programming languages like R and Python. Below are the steps for Twitter data sentiment analysis. 

1. Extract and collect data

To mine data from Twitter, you can use Twitter APIs like Tweepy and TextBlob. Data extraction is the crux of the process because all other steps directly depend on this. 

Ensure that your Twitter data contains the observations you want to analyze. You will find two types of tweets: 

• Latest tweets under real-time hashtags, keywords, or topics. 
• Historical tweets posted in the past and their responses in different timelines. 

2. Pre-process sentiment analysis dataset

Tweets are mostly unstructured data and require extensive pre-processing before using them as training data for the sentiment analysis model. Data reduction is one way of preparing data for mining. 

This includes: 

• Cleaning all the noise from the data
• Deleting duplicate or meaningless tweets [eg, tweets shorter than three characters]
• Formatting improvements and concatenation
• Preparing custom data to perform testing on the model

Clean and good data facilitates precise outcomes, i.e. more accurate predictions.

3. Build an ML model for sentiment analysis

The model building depends on the problem statement, requirements, and use cases. However, it has 5 mandates –

1. Create the base model type as a Classifier to categorize and define tags
2. Under the model, create a sentiment analysis project for the classification
3. Import the pre-processed data as the training dataset for model
4. Train the model by tagging each tweet as positive, negative, or neutral. After a few manual tagging, the models learn to do the tagging with maximum accuracy. 
5. Test the model performance and preciseness of predictions. The larger dataset you use, the easier it is for the model to learn the tagging. 

Tip for maximum accuracy: Define tags more explicitly in the training data and crosscheck for false positives and negatives using various test cases. 

4. Analyse the data for sentiment analysis

Integrate Twitter data with the tweet sentiment analysis model. There are pre-built APIs that require tokenization and API calls to complete the integration process. 

Examples: Inference API by Huggice Face, Lexanalytics, Brandwatch, Rosette, Social Mention, and more. 

5. Use data visualization to demonstrate your findings

Data visualization illustrates any sentiment analysis report the best. It is easy to understand, interactive, and dynamic. In addition, they work on business intelligence frameworks and render impeccable data visuals. 

Examples: Power BI, Google Data Studio, Tableau, Klipfolio, etc. 

Closing statement

Social sentiment analysis has proven benefits like – 

1. Large-scale data sorting
2. Real-time data analysis
3. Improved user experience
4. Detailed competitor analysis

It can be applied to innumerable aspects of your business – be it brand monitoring, product analytics, market research, and customer service. Social sentiment analysis helps brands work fast and work smartly. It works towards listening to users more accurately and making the end-user experience worthwhile. 

Sentiment analysis is already mandated for brands who want to build products or design services catered to humans. It gives a deeper insight into user intent, emotions, and engagements for teams to work more effectively. 

Have you started analyzing your users’ emotions across social channels?

Featured image by Luis Quintero on Pexels

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