Top 12 Sentiment Analysis Projects and Datasets (Updated for 2024)
Most Common Sentiment Analysis Approaches
Data Science itself can be quite broad, as it covers a large range of tools and applications, but one area to be specifically covered here is Natural Language Processing (NLP). Broadly, NLP is a field of data science that improves the ability of computers to analyze and understand human languages.
NLP’s best known application is sentiment analysis. Sentiment analysis detects positive or negative sentiment (emotion) in a piece of text, then uses these sentiments to make better business and design decisions. Data scientists use sentiment analysis projects and datasets to predict the polarity of a text, detect feelings, and even test interest. With those goals in mind, the three most common approaches to sentiment analysis are:
Graded Sentiment Analysis: An approach used to detect the positivity or negativity (polarity) of a piece of text.
Emotion Detection: An approach used to detect the overall emotional feeling (e.g., happy, sad, angry) from a piece of text.
Aspect-based Sentiment Analysis: An approach used to detect an aspect of your business (e.g., customer service, product design) being referred to either positively or negatively in a piece of text.
Practicing sentiment analysis in a data science project can be exciting and fulfilling for both NLP beginners and experts. This article will suggest the top 12 sentiment analysis projects and datasets that you can work on regardless of your NLP-knowledge level.
Graded Sentiment Analysis
1. Product Reviews Analysis
Let’s start with a simple project suitable for beginners in the field: building an analyzer for product reviews. These days most of us shop online quite a bit, and some customers choose to write reviews about their purchased products. These reviews can help other customers decide whether or not to buy this product, can help the company to make the product better, or can help the shopping website in considering whether to discontinue offering this specific product.
With so many stakeholders and use cases, product review analysis becomes ever more central for e-commerce companies’ growth. Try your hand at building a product review analyzer on product review datasets for Amazon, GameStop, or even McDonalds, and explore what insights the feedback can provide to the businesses.
2. Twitter Feed (Hashtag) Analysis
If you have ever looked up sentiment analysis online, chances are you’ve come across a project analyzing Twitter feeds. This is an excellent project because there are millions of public tweets on Twitter every day, as well as being housed by various APIs that work to collect content. You can use a Twitter crawler or an API source to build a dataset of a portion of these tweets and analyze them.
A great project is to build a tweet dataset on a specific topic or hashtag, and categorize each tweet’s sentiment as positive or negative, with the ultimate goal of forming an aggregated sentiment on the topic as a whole. Try your hand with this dataset of airline traveler tweets on their experiences with major US airlines. Or, check out a dataset of customer help requests submitted over Twitter for major retail brands. For a topic that is of more immediate interest to you, use a Python library like Tweepy to gather your own tweets for analysis.
3. WhatsApp Group Chat Analysis
Another exciting project if you’re a beginner or an intermediate-level data scientist is analyzing the sentiment of a WhatsApp group chat. You can collect the chat data for this project and then perform sentiment analysis on it.
Collecting your WhatsApp group chat data is not very difficult. You can either collect it yourself or use a sample dataset. This dataset challenges you to perform sentiment analysis on individuals within the group or on the group as a whole. If a group is dedicated to a wider perspective, try performing sentiment analysis on a specific topic that frequently arises.
4. Movie Reviews Analysis
If you’re a movie fan, this is the project for you. Sentiment analysis can be used in movie reviews to detect the general tone of what people think of a specific movie. To build this movie review project you can either use IMDb or Rotten Tomatoes. IMDb is an entertainment review website where people leave their opinions on different films and shows. Two datasets are provided here: the Large Movie Reviews Dataset with over 45k reviews, or the Rotten Tomatoes reviews dataset. The number of reviews has exploded alongside the rise of these platforms, and more recent movies have substantially more reviews than older releases.
5. Book Sentiment Analysis
As a book lover, I always look for ways to leverage what I already love to learn new things. So, if you like books and novels, you can build a sentiment analyzer for your favorite book and learn all the basics of sentiment analysis as you do so. You can do that by downloading your favorite book as a pdf and then processing and manipulating the text. You can find a similar project using R here.
6. TripAdvisor Reviews Analysis
For travelers, TripAdvisor can help make the correct decisions on what hotels to book, sights to see, and packages to buy. TripAdvisor is one of the most prominent websites for travelers, with reviews on various aspects of travel. Analyzing the sentiment of these reviews can help both travelers and TripAdvisor decide on worthwhile trips and packages to take or to offer. You can use this dataset to analyze the reviews of more than 20k hotels worldwide, and help plan your own dream vacation.
Emotion Detection
7. Stock Prices and Sentiment Analysis
The movement of stock markets is one of the most scrutinized economic indicators in the world. Markets are designed to be efficient, that is, the information underpinning stock prices is meant to be available to all participants at the same time and at the same scope, but this is rarely if ever the case. Because markets are inefficient, and information dictating stock prices is unevenly distributed among participants, gaining access to new information in order to predict stock prices gives an analyst immense leverage; fortunes are made on this kind of predictive power.
Data scientists using sentiment analysis have a unique tool in assessing information in markets. On platforms like Twitter, thousands of pieces of investor sentiment are generated every second on a huge range of listed companies and current prices, you only need to collect it and analyze it. Through sentiment analysis we can take these tweets about a company and judge whether they are generally positive or negative. This sentiment allows us to create predictions on a companies value, as stock prices often track with investor feeling. Take a look at the following graph to see how the two move together, and think about how you might be able to act on sentiment analysis with a real market.
TSLA stock prices Monday-Friday. The sentiment (originally scored from -1 to +1 has been multiplied to accentuate +ve or -ve sentiment and centered on the average stock price value for the week.
8. Company Reputation Analysis
The last project on our list is a company reputation analysis. When we apply for jobs, we often hope for more than just the title or salary of the role we apply to. We look for a company with a mission and purpose to give the work meaning, and a healthy work culture to help you grow and reach your full potential. Sentiment analysis will help you understand public opinion on the company and its products, or the internal environment from current and former employees.
To gauge external perceptions, applying sentiment analysis to social media sites like Twitter or LinkedIn can help assess how the company is perceived, and whether its stated mission is taken to be authentic. To assess the internal culture, sites like GlassDoor can be scraped and analyzed using sentiment analysis to get a sense for how employees feel about their own workplaces. For internal culture, bucketing by current vs. former employee status can provide additional insight into why people feel the way they do, and what to expect if you do end up working there. For either perception, sentiment analysis can also be useful to analyze sentiment over time, to see how the companies’ trajectories have risen or fallen, and what that might portend about a future working there.
9. Detecting Cyberbullying on Twitter by Analyzing Online Behavior
If you’re interested in exploring the impact of online behavior, particularly in the context of cyberbullying, this dataset provides a valuable opportunity. The dataset contains tweets labeled according to different types of cyberbullying or classified as non-cyberbullying. Analyzing such data allows you to delve into the nuances of harmful online interactions and understand the characteristics that distinguish cyberbullying content.
A compelling project would involve categorizing the tweets in this dataset according to the type of cyberbullying they represent, such as “hate speech,” “insults,” or “threats.” You could use this analysis to train a model capable of detecting cyberbullying in real-time, or to generate insights into the prevalence and nature of online harassment. This project not only provides practical experience in natural language processing but also contributes to the broader goal of making online spaces safer.
Aspect-based Sentiment Analysis
10. Scientific Paper Analysis
If you are a grad student or in academia and know the basics of machine learning, you can use sentiment analysis to review and evaluate scientific papers. For example, you can perform a sentiment analysis on the overall sentiment of the papers, gleaning how the authors feel about the topic at hand. You could also break down paragraphs into their component sentences to see how the authors feel about separate aspects within the broader research or for easier classification and analysis of sentences on their own.
You can also use sentiment analysis to find related papers and compare them, with the goal of identifying successful patterns for submission to academic journals. This dataset contains 14k+ scientific paper drafts, 10k paper peer reviews, and the ultimate accept/reject decisioning of papers at submitted journals. By applying machine learning basics, analyzing this dataset can help you get your project on scientific papers started and begin to understand what makes for an effective paper in academia.
11. Stack Overflow Question Closure Prediction
As programmers or people in tech, you must have used Stack Overflow at least once before (if not daily). On the platform you can find an answer to almost any programming question you may have, or to a similar enough problem that can then be transferred to your specific question. Because anyone can post questions on Stack Overflow, some questions are repetitive and may cost other programmers time and effort to reach the answer they desire.
This project aims to predict whether a new question will be closed (no longer able to be updated with new answers) or remain open. By scoring each new question based on the most common reasons a question is eventually closed (duplicate question, off-topic, subjective, not a real question, and too localized), future developers can produce the most effective platform possible.
12. Fuel Efficiency Trends Through Aspect-Based Analysis of Automotive Attributes
If you’re interested in automotive history or fuel efficiency trends, analyzing a dataset of car attributes can be a fascinating project. This type of analysis allows you to explore how vehicle characteristics like engine size, weight, and horsepower relate to fuel efficiency, as measured in miles per gallon (MPG). With a dataset such as the Auto MPG dataset, you can dive into the details of cars from the 1970s and 1980s, a period known for significant changes in the automotive industry, particularly with the oil crises influencing car design and fuel economy.
A great starting point for a project would be to explore the relationship between MPG and other car attributes. For example, you could analyze how factors like engine displacement and vehicle weight correlate with fuel efficiency, potentially visualizing these relationships using scatter plots or linear regression models. Additionally, you might look into the differences in fuel efficiency by origin or car model year, offering insights into how automotive design and technology evolved.
Conclusion
Sentiment analysis is an important and well-known branch of Natural Language Processing. The main goal of using sentiment analysis on any text is to analyze these segments of text and deduce the sentiment within. That means detecting feelings in the text or judging its overall tone (positive or negative). Sentiment analysis can be used in many fields, from detecting the general tone of a WhatsApp chat, to analyzing news articles, and even to predicting stock market prices.
If you are considering a career in data science or Natural Language Processing, having sentiment analysis projects in your portfolio can be a great addition. Sentiment analysis projects are often fun and interesting to build for people of all NLP-knowledge levels. This article went through 10 project ideas and datasets that will hopefully inspire your next sentiment analysis project.
More Data Science Project Ideas & Datasets
If you want to learn more about data science, projects are the perfect opportunity. You’ll find plenty of inspiration in our guides to data science projects:
Check out these project idea lists from Interview Query:
- 10 Python Data Science Projects
- 10 Regression Projects & Datasets
- 29 Machine Learning Project Ideas for All Levels
- 31 Free Datasets for Your Next Project
- 15 Marketing Analytics Projects and Datasets
- 10 Music Machine Learning Data Science Projects
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