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Sentiment Analysis Projects & Topics For Beginners [2024]

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Sentiment Analysis Projects & Topics For Beginners [2024]

Are you studying sentiment analysis and want to test your knowledge? If you are, then you’ve come to the right place. In this article, we’re discussing sentiment analysis project ideas with which you can test your knowledge and showcase your understanding.

We know how tricky it is to find great project ideas. We also know how beneficial it is to complete projects. With projects, you can strengthen your knowledge, enhance your portfolio, and bag better roles. 

The following article will talk about some of the best Sentiment Analysis Python project ideas. It will also shed some light on the various types, importance, and applications of Sentiment analysis in today’s world. By the end of it, you’ll be encouraged to work on prominent sentiment analysis and capstone project ideas.

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So without further ado, let’s get started. 

What is Sentiment Analysis?

Sentiment analysis is a kind of data mining where you measure the inclination of people’s opinions by using NLP (natural language processing), text analysis, and computational linguistics. We perform sentiment analysis mostly on public reviews, social media platforms, and similar sites. Following are the main types of sentiment analysis:


Fine-grained sentiment analysis gives precise results to what the public opinion is about the subject. It classified its results in different categories such as: Very Negative, Negative, Neutral, Positive, Very Positive. 

Detecting Emotion

This kind of sentiment analysis identifies emotions such as anger, happiness, sadness, and others. Many times, you’ll use lexicons to recognize emotions. However, lexicons have drawbacks too, and in those cases, you’d need to use ML algorithms

Based on Aspect

In aspect-based sentiment analysis, you look at the aspect of the thing people are talking about. Suppose you have reviews of a smartphone, you might want to see what the people are talking about its battery life or its screen size. 


Sometimes organizations need to analyze the text of different languages. This form of sentiment analysis is considerably challenging and requires a lot of effort because you’d need many resources. 

Sentiment analysis has many applications in various industries. As it helps in understanding public opinion, companies use sentiment analysis in doing market research and figuring out if their customers like a particular product (or service) or not. Then, according to the findings of the sentiment analysis, the organization can modify the respective product or service and achieve better results. 

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All in all, it helps companies in understanding their customers better. Companies can serve their customers better when they know where they lag and where they excel. 

Why Is Sentiment Analysis Required?

Before we delve into the various sentiment analysis project ideas, such as Twitter sentiment analysis project idea, or sentiment analysis of IBDb reviews, let’s take a look at some of the reasons why Sentiment analysis is important. 

In this technology-driven world, a majority portion of the data that we come across is unstructured. Whether it is in the form of emails, texts, or documents, the said data need to be properly structured and then analyzed further. This is where sentiment analysis comes into play. It not only helps to store data in an efficient and cost-friendly manner, but you can also solve certain real-time issues with the help of the same. 

Various Approaches Used In Sentiment Analysis

Broadly, there are three main approaches to sentiment analysis. They are-

  • Rule-based approach- Unlike the other approaches, the rule-based approach is quite easy to comprehend. It basically counts the total number of negative and positive words present in the data set. Following this, if the result indicates that the number of positive words is more than the number of negative words, then the sentiment is positive, and vice versa. 
  • Automatic Approach- In this approach, the data set is initially trained, following which predictive analysis is done. After completion of this stage, words are extracted from the text. This can be done with the help of various techniques, some of which might include Linear Regression, Support Vector, and Naive Bayes, among others.
  • Hybrid Approach- As the name suggests, this approach is basically an amalgamation of both the rule-based approach and the automatic approach. It delivers more accurate results when compared to the other approaches. 

Applications of Sentiment Analysis

There is a wide range of applications for sentiment analysis. Some of them have been discussed in the following list. 

  • Social Media- The comments on popular social media sites such as Instagram, Facebook, and Twitter are analyzed and then furthermore categorized into different segments, such as positive, negative, and neutral. 
  • Customer Service- One of the perfect examples might include the comment section in the Google Playstore application, wherein comments from 1 to 5, are usually selected with the help of the various sentiment analysis approaches.
  • Marketing Sector- The marketing industry has benefited a lot from sentiment analysis. It has helped brand owners to understand the review of a product or service, and whether it has been categorized as good or bad by the consumers. 

In the following points, we’ve discussed some prominent sentiment analysis project ideas, pick one according to your interests and expertise:

Sentiment Analysis Project Ideas

The following are our sentiment analysis projects. Our list has projects for all skill levels so that you can choose comfortably:

1. Analyze Amazon Product Reviews

Amazon is the biggest e-commerce store on the planet. This means it also has one of the largest product selections available. Many times, companies want to understand the public opinion on their product and figure out what’s responsible for the same. For that purpose, they perform sentiment analysis on their product reviews. 

It helps them in recognizing the primary issues with their products (if there are any). Some products have thousands of reviews on Amazon while some others only have a few hundred. 

It is one of the most sentiment analysis projects because the demand for such expertise is very high. Companies want experts to analyze their product reviews for market research. 

You can get the dataset for this project here: Amazon Product Reviews Dataset.

Working on this project will make you familiar with many aspects of sentiment analysis. If you’re a beginner, you can start with a small product and analyze reviews of the same. On the other hand, if you’re looking for a challenge, you can take a popular product and analyze its reviews. 

2. Rotten Tomatoes and Their Reviews

Rotten Tomatoes is a review site where you’ll find an aggregate of critics’ opinions on movies and shows. You can find reviews on nearly every show, TV series, or drama there. Admittedly, it’s also a great place to get data from. 

You can perform sentiment analysis on the reviews present on this site as a part of your sentiment analysis projects. The entertainment sector takes critic reviews very seriously. By analyzing critic reviews, a production company can understand why its particular title succeeded (or failed). Critic reviews influence the commercial success of a title considerably as well. 

With sentiment analysis, you can figure out what’s the general opinion of critics on a particular movie or show. This project is an excellent way for you to figure out how sentiment analysis can help entertainment companies such as Netflix. 

You can get the dataset for this project here: Rotten Tomatoes dataset

3. Twitter Sentiment Analysis

Twitter is a great place for performing sentiment analysis. You can get public opinion on any topic through this platform. This is one of the intermediate-level sentiment analysis project ideas. You should have some experience in performing opinion mining (another name for sentiment analysis) before you work on this task. As it’s a popular project idea, we’ve discussed in a little more detail:


You should have a basic knowledge of programming. You can either be familiar with Python or R (it’d be great if you’re familiar with both). However, it’s not necessary to have expert-level knowledge of programming. Apart from programming, you should also know how to split datasets and use the RESTful API because you’ll have to use Twitter API here. You should also be familiar with the Naive Bayes Classifier as we’ll be using it to classify our data later in the project. 

This project isn’t easy, and it’ll take a little time (downloading data from twitter takes hours). 

Working on the Project

First, you’ll need to get authorized credentials from Twitter to use the Twitter API. It takes some time to authorize a Twitter Developer Account, but once you have it, you can go to your dashboard and ‘Create an app’.

After you have the necessary credentials, you can create the function and build a test set. Twitter has a limit on the number of requests one can make through their API, which they have added this limit for security reasons. The ceiling is 180 requests in 15 minutes. You can keep the test set to have 100 tweets.

After creating the test set, you’ll have to build the training set by using Twitter API, which is the hardest part of this project. Make sure that you save the tweets you gather from the API in a CSV file for future use. 

After preparing the training set, you only have to preprocess the tweets present in the datasets. Remember, emojis, images, and other non-textual components don’t affect the polarity of sentiment analysis. To include pictures and other parts in your sentiment analysis, you’ll have to use Deep Learning. Make sure that you remove all the duplicate characters and typos from your data. Data cleaning is vital to get the best results possible.

After cleaning the data, you can use the Naive Bayes Classifier for analyzing the dataset available. Finally, you’ll have to test your model and see if it’s producing the desired results or not. 

This Twitter sentiment analysis project can help you gain practical experience in handling real-world data, applying sentiment analysis techniques, and interpreting results, making it a valuable project for those looking to enhance their skills in data science and natural language processing. 

As you may have realized, this project will take some effort. But performing sentiment analysis on Twitter is a great way to test your knowledge of this subject. It’ll be a great addition to your portfolio (or CV) as well. 

Read more: Sentiment Analysis Using Python: A Hands-on Guide

4. Reviews of Scientific Papers

If you’re interested in using knowledge of machine learning and data science for research purposes, then this project is perfect for you. You can perform sentiment analysis on reviews of scientific papers and understand what leading experts think about a particular topic. Such a finding can help you research them accordingly. 

Here’s the dataset so you can get started on this project: Machine Learning Dataset. The dataset we’ve shared here has N = 405 instances. And it’s stored in JSON format. Working on this project will make you familiar with the applications of machine learning in scientific research. The dataset has some reviews in Spanish and some in English.

5. Analyze IMDb Reviews

IMDb is an entertainment review website where people leave their opinions on different movies and shows. You can perform sentiment analysis on the reviews present there as well. Just like the Rotten Tomatoes project we discussed previously, this one will help you learn about the applications of data science and machine learning in the entertainment industry.

Reviews of shows and movies help production companies in understanding why their title failed (or succeeded). 

The dataset for this project is quite old and small. But it’s an excellent way for a beginner to test his/her skills on a new dataset. Here’s a link to the dataset: IMDb reviews dataset.

6. Analyze a Company’s Reputation (News + Social Media)

You can pick a company you like and perform a detailed sentiment analysis on it. You can also choose a trending topic and cover it in your sentiment analysis for a more precise result. We can discuss the example of Uber here. They are one of the most prominent startups in the world and have a global customer base. You can perform a sentiment analysis to understand public opinion on this company.

To find the public opinion on Uber, we’ll first start by getting data from the relevant sources, which in this case are Uber’s Facebook page and Twitter page. By analyzing the conversations between the users there, we can figure out the overall brand perception in the market. You’ll need categories to separate different datasets. In this example, you can use Payment, Service, Cancel, Safety, and Price. 

Now that we know what we want to work on and where we have to go, we can get started.

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Sentiment Analysis on Facebook

We’ll first begin with their Facebook page. It has more than 30,000 comments, and after we perform the analysis under the categories we mentioned previously (Payment, Service, Cancel, Safety, and Price) we found that most of the positive comments were about the Price section. On the other hand, the category with the highest percentage of negative feedback was service. However, while performing this analysis, we also kept in mind that Facebook’s comments are filled with spam, suggestions, news, and various other pieces of information. 

For sentiment analysis, we only have to look at opinions. 

So, we removed all the unnecessary categories, and as expected, our results changed. Now, negative comments held a majority in all sections, and their ratio in respective categories changed. In Price related comments, the percentage of negative comments rose by 20%. 

That’s why it’s essential to perform data cleaning. It helps you get accurate results. 

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Sentiment Analysis on Twitter

We’ve already discussed the sentiment analysis of tweets in this article. So we’ll follow a similar approach here and analyze people’s tweets where they tag Uber or reply to their tweets. Here, the category with the highest percentage of positive tweets was Payment, and the second-highest was Safety. This also shows how different social media give different results. 

However, we would have to perform data cleaning here as well. For that purpose, we’ll remove tweets with unrelated intent (spam, news, marketing, etc.). You’d notice how much the percentage of different categories changes here too. 

In our case, Payment saw a decline of 12% in its share of positive tweets and Safety became the category with the highest percentage of positive responses. Apart from that, Safety lost around 2-4% in its share of positive tweets. With this data, you can also find out what are the most popular topics among people when they talk about Uber on these platforms. 

So, on Twitter, we found that the most popular categories were payment, Cancel, and service. 

You should know that brands take this data very seriously. It helps them figure out what problems they need to work on and how they can solve the same. These tweets are, after all, feedback of customers. In this case, Uber can use the findings of these tweets to understand which parts of its services have faults and how they can fix them. 

Sentiment Analysis of News

To understand the public opinion on any organization, you’ll have to analyze the news about it as well. In our example, we’ll check the news articles about Uber. After we analyze the content present in those news articles, we’ll segregate our findings in the categories mentioned above (Payment, Service, Cancel, Safety, and Price). 

Apart from that, we’ll also classify different articles according to their popularity. The more popular an article is, the more it’ll affect public opinion. You can measure the popularity of every article according to the number of shares they have. A column with higher shares would undoubtedly be more popular than one with fewer shares. 

Also Read: Top 4 Data Analytics Project Ideas: Beginner to Expert Level 

The Results

In our example, we looked at Uber and the public opinion on this company. After we’ve analyzed Facebook, Twitter, and news, we’d know whether the general sentiment on Uber is positive, negative, or neutral. 

You can follow this approach to create sentiment project analysis ideas. You can start with a small company that doesn’t have a high online presence and performs sentiment analysis on multiple channels to understand if it’s perceived positively or negatively. If you want to increase the challenge, you can make it more complicated and perform analysis for a major company (like we did in our example).

7. Hate Speech Detection Model

Apart from the Twitter sentiment analysis project topic, the hate speech detection model is yet another very interesting area to explore in sentiment analysis python. Hate speech basically refers to any kind of communication or language used against a person, or a group, based on their sexuality, race, color, and religion, among other factors. This includes all kinds of verbal, in verbal, written or behavioral communication. The main task of the hate speech detection model is to identify and classify the hate speech from a given text. The same can be achieved by training the model on data, which is used to classify sentiments. 

Along with these, you can further explore many sentiment analysis and capstone project ideas following your enrollment in relevant degree or certificate programs, such as the ones offered on upGrad.

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8. Education Course Reviews

Analyzing sentiments in reviews for online courses or educational resources can be a valuable sentiment analysis projects for final year. It involves delving into student perspectives on learning experiences. By collecting and processing diverse datasets from platforms like upGrad, this project can help young learners understand the concept better. 

Most importantly, this is a sentiment analysis project using Machine Learning and NLP. Students can learn how to employ sentiment analysis techniques, such as Natural Language Processing and Machine Learning, to categorize reviews as positive, negative, or neutral. The analysis extends to identifying common themes and topics within the reviews, shedding light on aspects like engaging content or challenges with instructional clarity. A noteworthy aspect of the project involves exploring potential correlations between sentiment expressions and academic performance metrics, such as grades or completion rates. 

The findings can contribute meaningful insights into the factors influencing student satisfaction and success in online education. Ethical considerations, including data privacy, are essential throughout the project, and the results can inform recommendations for improving the quality of online courses and educational resources. 

All in all, this project on sentiment analysis supported by visualizations and comprehensive reporting provides a nuanced understanding of the student experience in virtual learning environments.

9. Social Media Influencer Impact

Analyzing sentiments in social media posts related to influencers involves studying the emotions and opinions expressed by users regarding specific influencers. The is a great example of a sentiment analysis NLP project that entails collecting a dataset of social media posts, comments, and mentions related to various influencers across social media platforms. NLP techniques are then applied to analyze the textual content, categorizing sentiments as positive, negative, or neutral.

The impact on followers is a key aspect of sentiment analysis projects like these. By correlating sentiment analysis results with engagement metrics, such as likes, shares, and comments, researchers can gauge how influencers affect their audience. It’s essential to explore the reasons behind sentiment shifts, such as the influencer’s content, behavior, or external factors.

This project on sentiment analysis has practical applications for marketing and brand management. Businesses and influencers can use the findings to understand their online presence, identify areas for improvement, and tailor content to better resonate with their audience. 

10. Sports Match Analysis

This sentiment analysis project begins by collecting a dataset of social media content related to specific sports matches, encompassing platforms like Twitter, Facebook, or dedicated sports forums. Natural Language Processing (NLP) techniques are then applied to analyze the textual content, categorizing sentiments as positive, negative, or neutral.

The analysis extends to exploring fan reactions, team sentiments, and the overall sentiment landscape during different phases of a sports match. Researchers may investigate how events within a match, such as goals, controversial plays, or game-changing moments, influence public opinions. For instance, spikes in positive sentiment may occur when a team scores, while negative sentiments may arise in response to referee decisions or unfavorable outcomes.

Visualizations, such as sentiment trend graphs or heatmaps, can be used to illustrate the ebb and flow of sentiments over time. Learners working on this sentiment analysis project can get enough knowledge coupled with hands-on experience to contribute to social media strategies for sports marketing, fan engagement. They can even inform sports commentators and analysts about the impact of events on public sentiment.

Movie Trailer Reactions

Analyzing sentiments expressed in comments or social media posts related to movie trailers offers a captivating project that provides valuable insights into audience anticipation and excitement for upcoming films. This sentiment analysis using Machine Learning project involves collecting and preprocessing textual data from various platforms, such as YouTube or Twitter, where movie trailers are shared. By applying sentiment analysis techniques and potentially incorporating emotion analysis, the goal is to categorize audience reactions as positive, negative, or neutral. 

Word clouds or sentiment distribution charts are the key visualizations of this project. They can then be used to present a comprehensive overview of audience sentiments. For a more advanced project, predictive modeling may be explored to estimate a movie’s potential success based on the sentiments expressed in the trailer reactions. 

This sentiment analysis using Machine Learning project report  offers filmmakers and movie studios actionable insights into the effectiveness of their promotional campaigns, audience engagement levels, and trends within the film industry, making it a valuable endeavor for learners interested in the intersection of data analysis and the entertainment industry.

Financial News Sentiment Analysis

Analyzing sentiments in financial news articles and social media posts related to the stock market or specific companies constitutes a dynamic project with multifaceted components. The project involves collecting a diverse dataset encompassing financial news and social media content, followed by meticulous text preprocessing to ensure data quality.

Employing sentiment analysis techniques, such as natural language processing or machine learning models, allows the categorization of sentiments into positive, negative, or neutral, providing insights into the overall sentiment landscape surrounding particular companies or the stock market. The analysis extends to examining the impact of sentiments on market trends and investor behavior, exploring correlations between sentiment trends and stock price movements, and identifying influential events in the financial world.

Additionally, the project delves into the influence of social media sentiments on investor decisions and may even include building predictive models for estimating stock price movements based on sentiment analysis results.

Ultimately, this sentiment analysis project with source code offers practical applications for investors, financial analysts, and companies, providing a nuanced understanding of how sentiments shape the complex landscape of financial markets.

Why Should Learners Take Up Sentiment Analysis Projects?

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Taking up a project on sentiment analysis can be highly beneficial to both beginners and final yearv students, These projects empower them with practical skills, industry relevance, and the ability to make a worthy impact, fostering a holistic learning experience. Here are a few reasons why you should give these projects a go:-

  • Practical application of skills: A sentiment analysis project provide learners with a hands-on opportunity to apply theoretical concepts and skills learned in areas such as natural language processing, machine learning, and data analysis. Engaging in a real-world project allows learners to bridge the gap between theory and practical implementation.
  • Skill development: Working on a sentiment analysis project with source code can help you develop a diverse set of skills, including data collection, data preprocessing, machine learning model implementation, and result interpretation. These skills are highly transferable and applicable in various domains.
  • Understanding data context: Sentiment analysis projects often involve analyzing text data from diverse sources. For example, a sentiment analysis Python project can help students learn the nuances of that language, context, and cultural variations present in real-world data. Understanding these complexities is crucial for accurate sentiment analysis.
  • Problem-solving and critical thinking: You will find numerous sentiment analysis projects with source code that will require you to formulate research questions, design methodologies, and make decisions on data preprocessing and model selection. Engaging in such projects enhances problem-solving skills and encourages critical thinking.
  • Portfolio building: Completing sentiment analysis projects for final year can help expert learners, ready to step into their professional lives build a strong portfolio showcasing their practical skills. A portfolio is a valuable asset when applying for jobs or pursuing further education, as it demonstrates hands-on experience and the ability to work on real-world problems.
  • Industry relevance: Sentiment analysis is widely used across industries for customer feedback analysis, market research, and brand management. By working on a sentiment analysis Python project, NLP project, ML project and the like, you can gain insights into industry-relevant applications of their skills, making them more attractive to potential employers.
  • Stay updated with technology: The field of sentiment analysis is dynamic, with ongoing advancements in techniques and tools. Engaging in an sentiment analysis NLP project or any sentiment analysis project using Machine Learning or Python, can help learners keep abreast of the latest developments in the field and encourages a mindset of continuous learning.
  • Communication and presentation skills: Summarizing and presenting the findings of sentiment analysis projects require effective communication skills. Learners have the opportunity to practice articulating complex technical concepts in a clear and concise manner.
  • Personal interest and motivation: Sentiment analysis projects can be chosen based on personal interests, making the learning process more engaging and motivating. Learners are more likely to invest time and effort when working on projects that align with their passions.
  • Contribution to knowledge: Sentiment analysis projects can contribute to the broader understanding of sentiment patterns in various domains. Learners have the opportunity to make meaningful contributions to research and gain a sense of accomplishment.

Final Thoughts

Sentiment Analysis is an essential topic in machine learning. It has numerous applications in multiple fields. If you want to learn more about this topic, then you can head to our blog and find many new resources.

On the other hand, if you want to get a comprehensive and structured learning experience, also if you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1What problems does sentiment analysis solve?

Sentiment analysis is becoming a crucial tool for monitoring and understanding client sentiment as they share their opinions and emotions more openly than ever before. Brands can know what makes clients satisfied or frustrated by automatically evaluating customer feedback, such as comments in survey replies and social media dialogues. This allows them to customize products and services to match their customers' demands. For example, employing sentiment analysis to examine 4,000+ surveys about your business could help you figure out if customers like your pricing and customer service.

2What are the challenges of sentiment analysis?

Even humans struggle to effectively interpret sentiments, making sentiment analysis one of the most difficult tasks in nlp. Every utterance is made at some moment in time, in some location, by and to some people, and so on. All statements are made in context. People convey their negative attitudes using positive phrases in irony and sarcasm, which can be difficult for robots to recognize without a detailed knowledge of the situation in which an emotion was expressed. Another difficulty worth tackling in sentiment analysis is how to handle comparisons. Another issue to overcome in order to undertake effective sentiment analysis is defining what we mean by neutral.

3How can you increase the accuracy of a sentiment analysis?

When working on a classification problem, it's critical to pick the test and training corpora wisely. Domain knowledge is required for a set of features to act in the classification process. In most data science situations, using a classification method on a cleaned corpora rather than a noisy corpus is advised. Keywords that appear infrequently in the corpus do not usually have a role in text classification. These infrequent characteristics can be removed, resulting in improved model performance. It's generally a good idea to reduce terms to their simplest versions. Lemmatization is the name for this method.

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