Sentiment Analysis: Everything You Need to Know

Sentiment analysis is a machine learning technique that uses NLP to identify positive and negative sentiment in text. Brands and organizations use it to detect user sentiments from feedback, measure brand reputation, and understand customers’ needs.

Customers are now more vocal than ever before about their experiences with brands, products, and services. There are also numerous websites, forums, and social media platforms that they can use to express their thoughts. It makes sentiment analysis an indispensable tool for monitoring customer feedback. You can automate the analysis of textual data such as reviews, social media conversations, and more. It can help you understand what aspects of your product please the customers and what frustrates them. You can use these insights to tailor products and services that live up to the customers’ expectations. 

For example, sentiment analysis can help you analyze 10,000+ reviews related to your product. You can use the insights to determine if the customers are happy with your product and customer service. 

You can also use it to analyze brand sentiment on social media platforms in real-time with ease. You can also identify unhappy customers and decrease your initial response time to retain them. 

The prospects and advantages of sentiment analysis are limitless. Let’s dive deeper to know more about sentiment analysis. 

Types of Sentiment Analysis

The sentiment analysis models focus on analyzing the sentiments expressed in any text. It dissects the emotions expressed by the authors and classifies the data into positive, negative, and neutral categories. 

Businesses and brands often use it to interpret customer feedback from multiple channels. There are various ways to develop sentiment analysis models that meet your requirements. Here are some of the most popular techniques of performing sentiment analysis:

Fine-grained Sentiment Analysis 

More polarity classifications, including positive and negative, can help you deal with complex polarity conditions in your organization.

• Very positive
• Positive
• Neutral
• Negative
• Very negative

This method of increasing polarity precision is known as fine-grained sentiment analysis. It is similar to the five-start rating reviews you usually find on hotels. 

• Very Positive = 5 stars
• Very Negative = 1 star

Emotion Detection 

This method of sentiment analysis focuses on detecting emotions. It identifies emotions such as happiness, frustration, anger, sadness, and more while analyzing text. It often uses lexicons (lists of words that carry emotions) or machine learning algorithms to examine data. 

Although, the biggest downside of using this method is that people express emotions in many different ways. Words that indicate negative thoughts can also be used to express positive feedback. 

Examples: 

Negative: Your customer support is soo bad, I regret buying your product.

Positive: This is bad-ass, I regret not trying it earlier.

Aspect-based Sentiment Analysis

When analyzing the sentiments in customer feedback, businesses want to know aspects of their product are often discussed and in what way. It can help them focus on improving the negative aspects and identify positive ones for marketing purposes. This is where aspect-based sentiment analysis can assist you. It can know what customers talk about the features of your product. For example: “The battery backup of this phone is not good”. Another example would be “The mountain hill hotel was good, but the food was really bad”.

Multilingual Sentiment Analysis 

Multilingual sentiment analysis is one of the most complex methods of sentiment analysis. It usually involves pre-processing a to of data to build the model. Most of the resources are available online (lexicons), but you will have to create some (translated corpora and noise detection algorithms). Furthermore, you must also have coding experience to build the algorithm. 

But if you want to avoid the hassle of building a sentiment analysis model from scratch, you can use BytesView instead. It has a language classifier, you can train custom sentiment analysis models with data related to your organization. You can then automate the classification of text in the language you want.

What makes Sentiment Analysis Important?

Today, customers use various online channels to share their experiences with various brands and products. But the sheer volume of that data makes it difficult to analyze. According to estimates, 90% of the data on the internet is unstructured. Sentiment analysis can help you automate the process of analyzing unstructured data from multiple sources. 

You can dissect valuable insights from large volumes of data created every day: emails, customer support tickets, customer feedback, social media posts, blogs, news articles, documents, and more. You can also use sentiment analysis to analyze the opinions of customers about your brand and products. 

Benefits of sentiment analysis tools:

• Sort Large Volumes of Data: Manually sorting massive volumes of data such as social media conversations, reviews, and surveys can be too time-consuming and inefficient. There is too much data to sort through and you will lose valuable time if you do it manually. With sentiment analysis, you can automate the process of analyzing large volumes of data efficiently and cost-effectively.

• Analyze Data in Real-time: Sentiment analysis can help you analyze user opinions in real-time. You can automate the classification of customer support tickets based on issues or queries. You can also analyze social media conversations related to your brand and campaigns. You can quickly identify key issues and take action in real-time to resolve them.

• Get a Consistent Criteria: People are frequently unable to assess the meaning of each piece of text with consistency. Humans have a success rate of 60-65 percent in determining the meaning of a text. Tagging text with sentiment is highly unreliable because it is subjective and influenced by personal experiences, thoughts, and beliefs. A centralized sentiment analysis system can benefit businesses by using the same data and criteria across all company-wide information, improving accuracy and analysis outcomes.

Sentiment analysis, also referred to as opinion mining, uses natural language processing to interpret human language and machine learning to identify the emotions expressed in textual data.

However, there are different algorithms that users can use to build a sentiment analysis model. It all depends on the volume of data you need to analyze and how accurate you want your model to be. Here are some of the commonly used sentiment analysis models:

• Rule-based: These models perform sentiment analysis based on a pre-determined set of rules.
• Automatic: These models leverage machine learning techniques to learn from data and increase accuracy.
• Hybrid: The models combine both rule-based and automatic sentiment analysis approaches. 

Rule-based Approach 

The rule-based approaches use a set of pre-determined rules to identify contradiction, subjectivity, or the subject from the text. The rules usually include the numerous NLP techniques related to computational linguistics. The most commonly used techniques are:

• Stemming, part-of-speech tagging (PoS), tokenization, parsing.
• Lexicons (list of words such as emotions, expressions, etc)

Here’s an example to give you an idea of how the system works: 

1. First, define lists of positive (bad, worst, poor, inferior) and negative words (good, great, best, excellent).
2. Next, identify and count the number of positive and negative words from the text. 
3. If the number of positive words is more than the text has a positive sentiment. Similarly, if the number of negative words is more than the text carries negative sentiments.

Although, the limitation that rule-based systems have is that they only consider the words but not the sequence in which it is arranged. You can use more advanced processing methods and add more rules for more accurate analysis. But adding more rules may drastically alter the past results and make the analysis model more complex. These systems also need regular fine-tuning and expansion of vocabulary along with regular investments to do so. 

Automatic Approach

Rather than a set of rules, automatic approaches use machine learning techniques to identify sentiments from textual data. A classifier, which accepts text as input and outputs one of several possible categories, such as positive, negative, or neutral, is frequently used to perform sentiment analysis.

Here’s how you can implement a machine learning classifier: 

Automatic Approach Training and Detection Method

In the training phase, the model pairs an input (e.g., a text) with its matching output (a tag) by learning to correlate them based on the training samples. Then the feature extractor transforms the text input into vectors. Next, it uses pairs of feature vectors and tags to train the machine learning algorithm for analyzing sentiments.  

The feature extractor is used in the prediction process to convert their unknown input to features. After running these feature vectors through the model, the machine will predict which tags are associated with it (positive, negative, or neutral).

Text Feature Extraction 

The primary step in building a machine learning text classifier is to transform textual data into vectors. The most common method to do it is bag-of-words or bag-of ngrams with their frequency. Although, a new feature extraction method known as word embeddings is gaining popularity. It makes it feasible to assign comparable representations to words with the same meaning, thus enhancing the performance of classifiers.

Text Classification Algorithms:

The text classification process includes statistical models like Linear Regression, Deep Learning, Naïve Bayes, or Support Vector Machines.

• Linear Regression: A statistical algorithm that predicts a variable (Y) based on a set of features (X).
• Deep Learning: Utilizing artificial neural networks to analyze data to resemble the human brain with a varied array of methods
• Naïve Bayes: A type of algorithm which employs Bayes’ Theorem to assign a text to a category
• Support Vector Machines: A text-point-based model that does not use probability and instead represents each instance of text using multiple dimensions. The different opinions found in that section of the chart are mapped to different regions. Documents are also labeled based on associations with previous documents and physical locations.

Hybrid Approach

Hybrid systems, as the name implies, bring together elements of rule-based and automatic systems. A major benefit of these methods is that they usually give more precise results.

Challenges Faced When Building a Sentiment Analysis Model

Sentiment analysis is probably the most difficult task in natural language processing. Even humans struggle to determine the sentiments precisely. Data scientists are creating better and more precise sentiment analysis models. But there are still some challenges that have yet to be overcome. Let’s discuss some of the challenges of sentiment analysis:

Text Subjectivity and Tone 

The textual data is usually of two types: subjective and objective. The subjective texts contain sentiments but the objective texts don’t. Let’s look at an example so you can have a clear understanding of both texts: 

The apple is delicious.

The apple is green.

Most people would consider the first sentence carrying a positive sentiment, whereas the second sentence will be considered neutral. Although, not all words (verbs, adjectives, and nouns) carry the same weight in terms of sentiments. In the above examples, the word ‘delicious’ is subjective and expresses sentiments. 

Text Polarity and Context 

Everything that can be said will be said by someone, in some place, at some point in time. But what can be said will be said in some context. Analyzing sentiments without context can decrease the accuracy of the analysis. Machines cannot learn to identify the context unless they are specifically mentioned. Another issue is the change in polarity. Let’s look at some examples to make the concept more clear: 

• Unquestionably all of it.
• Definitely nothing 

Now consider these as responses to the question, What is it that you liked about the game? The first response will be positive and the second negative. But what if we change the question? What if the question is, What is it that you don’t like about the game? The negative question alters the sentiment that the responses carry despite it remaining the same. 

It requires a great deal of pre-processing or post-processing to effectively analyze sentiments in the right context. But figuring out how to effectively filter and use data for understanding the context of sentiments is complicated.

Irony and Sarcasm in Text

Irony and sarcasm are the most difficult sentiments for machines to detect. People often communicate negative sentiments through positive words and vice-versa. Machines will produce inaccurate results if they do not know the context in which the sentiments are expressed. For example, let’s look at one such tweet:

“Raise your hand if you have ever felt personally victimized by @ErikaJSchultz’s photography skills” pic.twitter.com/KrgLYuMz4r

— Lindsey Wasson (@lindseywasson) January 30, 2020

What sentiment do you think the tweet carries? The tweet uses the negative word, “victimized”, which indicates negative sentiment. But if we look at the context, the author is praising the skills of the artist. The problem here is that the machine has no textual clue to help it learn from the data. 

Text Comparison 

Treating comparisons in sentiment is another major challenge that you might face. Let’s look at a few examples: 

• Nothing can beat this. 
• Older tools fall short of this. 
• Better than nothing.

The first sentence clearly carries clues that indicate positive sentiments. 

However, the second and third texts are a bit more complex and difficult to categorize. Is it positive, negative, or neutral? Here, the context in which the statements are made is vital to identify the sentiments. 

Emojis in Text

There are two types of emojis:

• Western Emojis: those encoded in one or two characters.
• Eastern Emojis: Those with a longer combination of characters of a vertical nature.

These emojis play a crucial role in expressing sentiments, especially in tweets. 

Character-level, and even word-level, considerations will need to be taken into account in your sentiment analysis of tweets. This is especially true when it comes to preprocessing. You might want to preprocess tweets and convert both Western and Eastern emojis to tokens. To improve sentiment analysis performance, you could whitelist (that is, always treat emojis as a feature in classification) these tokens.

You’ll need to pay special attention to character-level, as well as word-level when performing sentiment analysis on tweets. A lot of preprocessing might also be needed. For example, you might want to preprocess social media content and transform both Western and Eastern emojis into tokens and whitelist them (i.e. always take them as a feature for classification purposes) to help improve sentiment analysis performance.

A list of emojis and their Unicode characters for pre-processing. 

Determining Neutral Sentiments 

Other difficulties include comprehending how neutral works when attempting to increase sentiment analysis accuracy. Defining the neutral tag is critical in this classification problem. It makes a difference which category you place sentiment analysis models in (neutral, positive, or negative). The successful application of tagged data requires a strong characterization of the problem.

Here are some ways you can accurately define neutral texts: 

• Objective-sounding texts do not contain any specific sentiments. You can tag such texts in the neutral category.
• If you haven’t processed your data, it may contain irrelevant text which you can mark as neutral. However, only follow these instructions if you understand the ramifications for the entire project. The added noise can lead to a drop in accuracy.
• Generally, the text of the product contains impartial comments, such as the use of the phrase “I wish the software had more plugins.” Comparing products, such as saying I wish the software were better, is hard to classify.

Human Annotator Accuracy 

Performing sentiment analysis is a challenging task even for people. The average inter-annotator agreement (how well two or more humans make the same annotation decision) is low. Moreover, with classifiers being able to learn from data, sentiment analysis classifiers could become inaccurate, compared to other classifiers.

Nevertheless, even if you are occasionally incorrect, the advantages of sentiment analysis make it worth all the effort. You can use the BytesView sentiment analysis model to get accurate predictions with maximum accuracy. 

The benefits are easily noticeable if you are new to sentiment analysis. You will save money and time on tiresome manual activities by automating tasks like ticket routing, brand monitoring, and VoC analysis.

Now that you have a basic understanding of sentiment analysis, along with the various options available in the industry, you should dive further into the topic. Here are some information sources that you can check out.

Sentiment Analysis Datasets

The key to building an effective sentiment analysis solution is, analyzing various datasets and testing the different approaches. You need to accumulate a substantial volume of data to perform your research and testing.

Here are some datasets that you can use for experimenting with sentiment analysis. They are available for free download on the internet. 

• Restaurant reviews: 5.2 million Yelp reviews along with star ratings.
• Fine-dining reviews: Amazon’s dataset has roughly 500,000 meal reviews. And each review has a plain text version as well as product and user information.
• Product reviews: The dataset contains millions of Amazon customer reviews with star ratings, which is perfect for training sentiment analysis models.
• Movie rating tweets: This dataset comprises 1,000 positive and 1,000 negative reviews. It also includes 5,331 positive and negative processed remarks and phrases.
• Apple INC: This data set includes tweets about Apple Inc. It was gathered to examine user reactions about Apple INC.
• Stock market-related tweets: This collection is made up of tweets sharing financial news. Of the Twitter messages that were studied, 3,685 were positive, and 2,106 were negative.
• Although, if you are experimenting with rule-based sentiment analysis techniques, lists of lexicons can help you out. Here are a collection of lexicons (lists of words with labels indicating the sentiment they carry) that you can use to fuel your research and testing. 
• Sentiment Lexicons for 81 Languages: In this dataset, there are lexicons including both positive and negative sentiments in 81 languages.
• SentiWordNet: With around 29,000 words, it contains sentiment scores ranging from 0 to 1.
• Wordstat Sentiment Dictionary: Around 5000 positive and 9000 negative terms are found in this sample.
• Opinion Lexicon for Sentiment Analysis: The dataset contains 4,782 English terms that are considered negative, and 2,005 words that are seen as positive.
• Emoticon Sentiment Lexicon: A list of 477 emoticons, categorized as either positive, neutral, or negative, is in this dataset.

Sentiment Analysis Papers 

There are hundreds of thousands of academic papers, reports, and books about sentiment analysis. 

Many academics in the sentiment analysis domain point to the following articles as leading the field in various ways:

• Opinion mining and sentiment analysis (Pang and Lee, 2008)
• Emotion Recognition in Conversations
• Recognizing contextual polarity in phrase-level sentiment analysis (Wilson, Wiebe, and Hoffmann, 2005).
• Bilingual Emotion Lexicon
• A survey of opinion mining and sentiment analysis (Liu and Zhang, 2012)
• Multilingual Twitter Sentiment Classification
• Sentiment analysis and opinion mining (Liu, 2012)
• How to Perform Text Mining with Sentiment Analysis
• Systematic Reviews in Sentiment Analysis

Sentiment Analysis Courses 

Studying Natural Language Processing (NLP), the computer science domain that focuses on human language interpretation is another successful method of deep sentiment analysis. NLP enables machines to better understand the sentiment, assessments, attitudes, and emotions found in written language, which has a wide range of applications in everyday interactions. 

Stanford Coursera course by Dan Jurafsky and Christopher Manning is the fundamental NLP course. There are numerous resources and lectures available on the internet, but the Stanford Coursera course is the primary course required to learn NLP. This course introduces you to the subject through two of the most well-known NLP figures, who will guide you through an in-depth process. 

If you prefer more interactive learning, check out the Data Science: Natural Language Processing (NLP) in Python course by Udemy. This course will provide you with a thorough introduction to NLP and how it can be used. This will entail completing various Python-based projects such as a spam detector, sentiment analyzer, and article spinner. The course consists of approximately 5-minute lectures that are academic without being overwhelming.

Sentiment Analysis Books 

Bing Liu is a leading machine learning expert who specializes in sentiment analysis and opinion mining, topics covered in his book. 

Liu does an excellent job of explaining the complexities of sentiment analysis while keeping it simple for beginners. One example is his work on Sentiment Analysis, a popular machine learning method that can be used to compute student and teacher confidence levels as well as paraphrase recommendations. 

The Deep-Learning-Based Technologies for Sentiment Analysis book is intended for people who want to investigate sentiments using machine learning and AI approaches.

You can refer to books such as: 

• Natural Language Processing with Python 
• Sentiment Analysis for Social Media