Sentiment analysis, also known as “opinion mining,” uses natural language processing (NLP) to determine whether presented data is positive, neutral, or neutral. This process is applied to contextual data to assist businesses monitor product and brand sentiment. Experts analyze customer feedback like social media conversations and survey responses and report useful feedback.
If you want to help a company customize its product and service offerings to meet the needs of its customers, completing sentiment analysis projects is a great way to build your skillset and portfolio at the same time. If you’re ready to become an expert on all aspects of sentiment analysis, read on for project ideas designed for every skill level.
5 Skills That Sentiment Analysis Projects Can Help You Practice
Some of the best sentiment analysis projects can help you build the skills you need to start a career in the field and climb the corporate ladder. Here are some of the handy skills you’ll develop when you work on sentiment analysis projects.
- Social Media Monitoring. Sentiment analyzers have to work with machine learning software to wade through the social media data and analyze the overall public sentiment on every business’s social media platform. If you work on these projects, you will practice working with different learning software.
- Customer Support. With natural language understanding (NLU), sentiment analyzers read regular language in search of meaning, tone, emotion. The analysis will be used to understand varying customer requests. This information will be used as a basis to process emails, phone calls, online chats, and customer support tickets by sentiment.
- Customer Feedback. Through sentiment analysis, businesses read beyond words to identify sarcasm and understand common chat acronyms. If you work on a sentiment analysis project, you can also develop skills in studying and analyzing different customer feedback.
- Product Analysis. With sentiment analysis, you can figure out what the public is saying about a product. You can use keyword research related to a specific product feature and allow your sentiment analysis models to find the information you need.
- Text Analytics. This would keep you up to speed with implementing common text mining strategies and turning texts into numbers. With ample training, you can utilize powerful algorithms to analyze your company’s massive text databases.
Best Sentiment Analysis Project Ideas for Beginners
Great projects demand excellent skills. As a novice, you may not have big projects right away, but you will surely land one as you upskill. To do that quickly and move to the intermediate level, you can work on these sentiment analysis project ideas for beginners.
- Sentiment Analysis Skills Practiced: Text analytics, product analysis, machine learning, data preprocessing, developing sentiment classifier, logistic regression
Amazon is currently the most popular ecommerce platform with a comprehensive array of products. Often, independent vendors, companies, and even Amazon themselves want to gather public opinion on products. This is where sentiment analysis comes in. Positive sentiment about a particular Amazon product could highlight what the brand is doing right.
For this project, you must conduct sentiment analysis on reviews from various products online. The first thing to do is to recognize the present issues about the product. Your primary task is to make sense of these algorithms and rankings to effectively organize high and low reviews and note the common trends amongst specific rankings.
- Sentiment Analysis Skills Practiced: Text analytics, NLP, n-gram methodology, data review, data set training with Create ML, logistic regression, deep learning
Rotten Tomatoes users use the platform to share reviews on shows and movies. This software is vital for the entertainment industry, where users can search for reviews on almost every drama, tv collection, or film ever made. While ecommerce stores like Amazon rely on customer feedback, Rotten Tomatoes relies on public opinion.
Users can use classification methods to identify the different product opinions before the machine learning algorithm is reviewed and analyzed. Select a dataset and work to determine sentiment based on specific phrases used. Working on this project could soon make you a valuable asset to leisure companies like Netflix, Hulu, HBO, and more.
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- Sentiment Analysis Skills Practiced: Programming, natural language processing, dataset splitting, use of RESTful API, Naive Bayes Classifier, Twitter API
Using Twitter, you can gather public opinion on any topic online. For this project, you’ll use natural language processing (NLP) and Twitter API to go through thousands of tweets to find out the dominant customer sentiment. You’ll use open source libraries on GitHub like the Jupyter Notebook to conduct this Twitter sentiment analysis.
Use automated learning technology to conduct a global analysis of a topic or product opinions based on negative and positive tweets. While you can undertake this project at an intermediate level, it is a good way to learn approaches to sentiment analysis as a beginner. You can complete a more comprehensive analysis as you upskill and gain experience.
- Sentiment Analysis Skills Practiced: Knowledge of machine learning, data science, data analysis
If you have knowledge in data science and machine learning, you can use both of these vital skills to evaluate scientific and academic papers. You can perform a sentiment analysis on the perceptions of these papers and identify what experts consider when dealing with a specific matter.
By using a topic in machine learning, you can turn complex abstracts into lists that are much easier to conduct binary classification. This fundamental task will help you combine relevant papers based on a specified set of criteria for more thorough research.
- Sentiment Analysis Skills Practiced: Data science, machine learning, Pandas, Matplotlib
Like Rotten Tomatoes, IMDb is a platform where you can find the opinion of critics on various movies and television shows. Carrying out a sentiment analysis on this platform could help you practice your machine learning and data science skills to later work with big leisure companies like Netflix, Hulu, and HBO.
Critic reviews are as essential as customer reviews. The negative reviews a show or film gets reflect its quality and how the viewers perceive it. Sentiment in customer feedback can be insightful to movie producers, directors, and those involved in its creation. These insights can guide them on their future projects.
Best Intermediate Sentiment Analysis Project Ideas
Suppose you already have experience in sentiment analysis and you’re looking for more projects to help you improve your skills. These intermediate-level sentiment analysis project ideas will help you improve your abilities and become an advanced sentiment analyzer.
- Sentiment Analysis Skills Practiced: Programming, dataset processing, text analytics
Stack Overflow is every programmer’s best friend, with answers for nearly every type of coding question available through online forums. However, the questions raised by users are occasionally incomplete, time-wasting, or repetitive. When a question is judged to be unhelpful, it is closed by moderators.
If you’re planning to take on this project, you will be predicting whether a new question raised will be closed or not based on previously-submitted queries. You will also reveal the rationale behind each action. This can help prevent future time-wasting or unhelpful questions.
- Sentiment Analysis Skills Practiced: Text summarization, semantics preservation, method comparison
Those adept in NLP know that text summarization is an exciting machine learning model and topic to dabble in. It’s innately tricky for humans to extract extensive text document summaries. To remedy this, you can try automatic text summarization. This means you’ll have to identify and summarize meaningful information in documents while staying true to the overall meaning.
The project’s main objective is to develop a shorter variation of the original text while keeping the semantics. While working on this project, you can use advanced and traditional methods to identify which one is best to utilize for your purpose.
- Sentiment Analysis Skills Practiced: Text analytics, NLP, customer support, computational intelligence
Quora is a platform where users can post questions and gather answers from other users. You can find different kinds of information, all from questions asked by users across the globe. All content you see on the site is user-generated.
If you work on this project, you will classify whether or not specific questions are duplicates. You will then look for high-quality answers to these questions. As a result, you will organize Quora’s question bank and improve the platform’s user experience.
- Sentiment Analysis Skills Practiced: NLP, ML, artificial intelligence
This task aims to check, scrutinize, and compare two different text entities to identify whether or not they have the same meaning. Though this sounds simple at first, you’ll have to design an algorithm that not only compares these strings of letters but understands their semantic differences.
This project is applicable in summarization, information extraction, and automatic plagiarism detection. Here, you’ll be using various classification and similarity-based methods to differentiate two pieces of text correctly.
- Sentiment Analysis Skills Practiced: Knowledge in long short-term memory (LSTM), recurrent neural networks, machine learning, big data, artificial intelligence
In this project, you’ll analyze a massive dataset of tweets related to the COVID-19 pandemic. This kind of task will help identify and understand human psychology during the pandemic and what some of the trending topics and keywords at the time revealed about the human experience.
Further, this project aims to identify public mental health at the time of the global health crisis. The data is organized by time and date to provide detailed insights into temporal characteristics. By working on this project, you will learn how to gather, analyze, and interpret massive data to yield results that could be used as the basis of more important tasks.
Advanced Sentiment Analysis Project Ideas
Advanced sentiment analysis projects are meant for individuals who are already advanced in sentiment analysis but still want to improve their abilities. If you want to become an expert in sentiment analysis, here are some tasks you can try to boost your skills even further.
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- Sentiment Analysis Skills Practiced: Generative Pre-trained Transformer 2 (GPT-2), web scraping, Python, text bots
In this task, your aim is to come up with various scientific paper titles. This project uses Python to generate a training model based on other papers, tweets, and quotes to develop unique academic paper titles.
For this unique project, you will be using a tool called GPT-2 which could extract over 2,000 paper titles from arXiv. You can also use this helpful app for other text generating tasks like dialogue production, song lyrics generation, and more.
- Sentiment Analysis Skills Practiced: Web application building, Python, web translation tools
With this project, you will be developing a text highlighter tool that can identify commonly used phrases in a large set of Arabic texts. This tool will allow you to summarize important sentiments by identifying top-ranked sentences.
- Sentiment Analysis Skills Practiced: Use of Restful API, knowledge of NLP and API techniques
Checking for plagiarism is an essential part of any academic or professional paper, project, or presentation. In this project, you will learn to build a similarity-checking API tool with NLP techniques. You will be implementing NLP tools, and you’ll become more adept at building an NLP application.
- Sentiment Analysis Skills Practiced: Opinion mining, NLP, data set building, dataset building, machine learning, artificial intelligence
As its name suggests, you will be using NLP to detect fake news articles for this project. With the abundance of news stories online, weeding out incorrect facts and statistics will help identify trustworthy sources.
You will also be building data sets that will help you catch examples of fraudulent news. The complexity of the process and data sets make this ideal a challenge for advanced-level sentiment analyzers.
- Sentiment Analysis Skills Practiced: Machine learning, artificial intelligence, python, data cleaning
Hate speech is any form of digital communication that poses a threat or sends a hateful comment towards a minority group online. As a result, hate speech detection algorithms are crucial for social media sites like Twitter and Facebook.
This task involves sentiment classification. For training, you can achieve a model capable of classifying hate speech within a textual piece once you train it on a given data. This data will then be used to organize positive sentiments versus negative sentiments and flag inappropriate comments accordingly.
Next Steps: Start Organizing Your Sentiment Analysis Portfolio
Your prospective employer isn’t capable of knowing what you’re capable of doing unless you show them what you can do. This is exactly why you need to build an impressive portfolio showcasing your past projects that can prove to hiring committees that you have the skills they are looking for. Consider these simple yet helpful tips when building your portfolio.
Highlight your Best Projects
When you apply for a job, you need to put your best foot forward. You have to show your prospective employer the projects that you are most proud of and that best display your mastery of crucial sentiment analysis strategies and tools.
Include Relevant Experience
While you may get excited to show off your skills, you must ensure that your projects are related to the job you’re applying for. This can help your prospective employer gauge whether you’re a good fit for their organization and check if you have the skills necessary to perform well in the specific role they are trying to fill.
Keep your Portfolio Updated
You need to update your portfolio with new projects and experiences regularly. You also have to customize your new additions to align with the requirements of the job you’re applying for. An updated portfolio doesn’t only mean you’re on top of your game, but it also shows that you’re diligent enough to get to know your prospective employer and learn about the job requirements.
Sentiment Analysis Projects FAQ
Gathering feedback and reviews from customers and categorizing them as positive, negative, or neutral is an example of sentiment analysis. This data can then be used to create better products, advertising strategies, and more.
According to some experts, some of the best algorithms for sentiment analysis are the Naive Bayes and the XGBoost. These are known for having high accuracy over multiple tests. The best algorithm for you will depend on the specifics of your project.
Opinion mining or sentiment analysis experts will utilize NLP to determine whether certain data is positive, negative, or neutral. Sentiment analysis is done on textual information to yield results that could help companies monitor the sentiments of their customers through their feedback.
It is commonly believed that traditional learning methods like Support Vector Machines (SVM), Local Regression, and Naive Bayes are the best when conducting sentiment analysis for large-scale data. These tools are useful when it comes to scalability.
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