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Research topics based on sentiment analysis.
Sentiment Analysis for PhD Research Topics
- Sentiment analysis and text-based analytics automatically analyze a large amount of available data and extract opinions that may help customers and organizations achieve their goals.
- Sentiment analysis can complement other systems, such as recommendation systems, information extraction, and question-answering systems.
- Sentiment analysis combines various research areas, such as natural language processing, data mining, and text mining, as they strive to integrate computational intelligence methods into their operations and attempt to shed more light on and improve their products and services.
- With the advent of the Internet, various survey tools have become more readily available, but obtaining accurate and relevant data from customer surveys is a significant challenge.
- Due to the dynamic nature of sentiments, opinions change with changing competition, technology, use, and many others. The dynamic aspect of sentiment analysis becomes necessary to handle large data dynamically.
Types of Sentiment Analysis
What is the working process of sentiment analysis, major difficulties with sentiment analysis method includes;.
- It is quite challenging to determine if a statement is optimistic or pessimistic when the data is presented in a tone.
- Must determine if the info is beneficial or negative if it is shown as an emoji.
Importance of Sentiment Analysis
Overall benefits of sentiment analysis, why is sentiment analysis matters.
- Social Media Attention
- General and Special Forums
- User generated Product Reviews
- Responding to Customer Support (about products)
- Professional Product Reviews (such as Wired and Verge)
Applications of Sentiment Analysis
Future research directions of sentiment analysis.
Future research directions in sentiment analysis can encompass a wide range of topics. Some specific areas that researchers can explore: 1. Aspect-based sentiment analysis: Aspect-based sentiment analysis focuses on identifying and analyzing sentiments towards specific aspects or features of a product, service, or entity. Future research can delve into more advanced techniques for aspect extraction, sentiment classification at the aspect level, and understanding the relationships between aspects and sentiments. 2. Cross-domain sentiment analysis: Sentiment analysis models trained on one domain may not perform well in different domains due to domain-specific language and sentiment expressions that allow sentiment analysis models to transfer knowledge and generalize effectively across diverse domains. 3. Multilingual sentiment analysis: Sentiment analysis in multiple languages poses unique challenges due to language-specific nuances, sentiment expressions, and cultural differences. 4. Emotion detection: Sentiment analysis often focuses on positive, negative, or neutral sentiments, but emotions play a significant role in human communication. Emotion-aware sentiment analysis can provide a more nuanced understanding of user sentiment. 5. Sentiment analysis in social media: Social media platforms generate vast amounts of user-generated content with rich sentiment signals. Future research can focus on developing techniques to handle the unique characteristics of social media text, such as slang, abbreviations, hashtags, and user mentions. 6. Sentiment analysis in conversational data: Conversational data, such as chat logs or customer service interactions, pose specific challenges for sentiment analysis. Future research can explore techniques to analyze sentiment in conversational data, including sentiment tracking across dialogue turns, understanding sentiment in context shifts, and distinguishing individual sentiment from group sentiment in conversations. 7. Long document sentiment analysis: Most sentiment analysis research focuses on short texts, such as tweets or product reviews. However, analyzing sentiment in longer documents, such as articles or essays, presents challenges. Future research can investigate techniques for sentiment analysis in long documents, including hierarchical sentiment modeling, document-level sentiment summarization, and sentiment tracking across lengthy texts. 8. Sentiment analysis for low-resource languages: Many languages lack sufficient labeled data for sentiment analysis and focus on developing techniques to handle sentiment analysis in low-resource languages, including leveraging transfer learning, semi-supervised learning, or active learning approaches to overcome data scarcity challenges. 9. Deep learning advancements: Deep learning has shown promise in sentiment analysis, but there is room for further exploration. Future research can focus on developing more advanced deep learning architectures, such as graph neural networks or transformer-based models, to improve sentiment analysis performance, handle complex linguistic structures, and capture long-range dependencies.
- A review: preprocessing techniques and data augmentation for sentiment analysis-
- Optimizing Sentiment Classification for Arabic Opinion Texts-
- A Multiclass Depression Detection in Social Media Based on Sentiment Analysis-
- Multi-source data fusion for aspect-level sentiment classification-
- Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers-
- A survey on opinion mining and sentiment analysis: Tasks, approaches and applications-
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10 Sentiment Analysis Project Ideas with Source Code 
Explore some of the best sentiment analysis project ideas for the final year project using machine learning with source code for practice.
Emotions are essential, not only in personal life but in business as well. How your customers and target audience feel about your products or brand provides you with the context necessary to evaluate and improve the product, business, marketing , and communications strategy. Sentiment analysis or opinion mining helps researchers and companies extract insights from user-generated social media and web content.
Ecommerce product reviews - Pairwise ranking and sentiment analysis
Downloadable solution code | Explanatory videos | Tech Support
Irrespective of the industry or vertical, brands have become imperative to understand consumers’ feelings about the brand and products. With cut-throat competition in the NLP and ML industry for high-paying jobs, a boring cookie-cutter resume might not just be enough. Instead, working on a sentiment analysis project with real datasets will help you stand out in job applications and improve your chances of receiving a call back from your dream company.
Building a portfolio of projects will give you the hands-on experience and skills required for performing sentiment analysis. In this blog, you’ll learn more about the benefits of sentiment analysis and ten project ideas divided by difficulty level.
Table of Contents
What is sentiment analysis, beginner level sentiment analysis project ideas, intermediate level sentiment analysis project ideas, advanced sentiment analysis project ideas, top sentiment analysis project ideas with source code using machine learning.
Let's put first things first to understand what exactly is sentiment analysis and how it benefits the business.
Sentiment analysis is used to analyze raw text to drive objective quantitative results using natural language processing, machine learning, and other data analytics techniques. It is used to detect positive or negative sentiment in text, and often businesses use it to gauge branded reputation among their customers.
There are various types of sentiment analysis where the models focus on feelings and emotions, urgency, even intentions, and polarity. The most popular types of sentiment analysis are:
Fine-grained sentiment analysis
Aspect based sentiment analysis
Multilingual sentiment analysis
Sentiment analysis is critical because it helps businesses to understand the emotion and sentiments of their customers. Companies analyze customers’ sentiment through social media conversations and reviews so they can make better-informed decisions. The Global Sentiment Analysis Software Market is projected to reach US$4.3 billion by the year 2027. Between 2017 and 2023, the global sentiment analysis market will increase by a CAGR of 14%.
The overall benefits of sentiment analysis include:
Sorting Data at Scale: With sentiment analysis, companies don't have to sort through customer support conversations manually, thousands of tweets, and surveys. Sentiment analysis helps businesses process vast amounts of data efficiently.
Real-Time Analysis: It helps to identify critical issues in real-time. For example, is a crisis on social media escalating? Is there an angry customer about to churn? With Sentiment analysis models, businesses can immediately identify customer pain points and take action right away.
Consistent criteria: A centralized sentiment analysis system can improve accuracy and deliver better insights since tagging text by sentiment is highly subjective, influenced by personal experiences, thoughts, and beliefs.
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1. Amazon Product Reviews
The first beginner-friendly Sentiment Analysis project idea is about evaluating Amazon product reviews. Amazon is one of the biggest e-commerce stores, and it also has a wide product selection. When companies want to understand public opinion, performing sentiment analysis helps them recognize what customers like about their products. It also helps to figure out the primary issues with their products.
The sentiment analysis for this project begins from scraping raw alpha-numeric text of various products from Amazon. These reviews have to go through a text processing stage that primarily uses TFIDF (Term Frequency Inverse Document Frequency) to convert text into integers. Classification models like SVM (Support Vector Machines) can label a given sentence ‘Positive’ or ‘Negative.’
Expertise in this project is in demand since companies want experts to use sentiment analysis to analyze their product reviews for market research. A beginner can start with less popular products, whereas people seeking a challenge can pick a popular product and analyze its reviews.
The dataset for Amazon Product Reviews: Amazon Product Reviews Dataset .
Access the Sentiment Analysis Project on Product Reviews with Source Code
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2. Rotten Tomatoes Movie Reviews
Rotten Tomatoes is a movie and shows review site where critics and movie fans leave reviews. The platform has reviews of nearly every TV series, show, or drama from most languages. It's a substantial dataset source for performing sentiment analysis on the reviews.
The movie review analysis is a classic multi-class model problem since a movie can have multiple sentiments -- negative, somewhat negative, neutral, fairly positive, and positive. Since a movie review can have additional characters like emojis and special characters, the extracted data must go through data normalization. Text processing stages like tokenization and bag of words (number of occurrences of words within the text) can be performed by using the NLTK (natural language toolkit) library.
The entertainment industry takes critic reviews seriously, and it also helps production houses to understand why their series or movie succeeded (or failed). Critic reviews also influence the commercial success of a drama and movie since people check reviews before booking their movie tickets. With sentiment analysis, production houses can figure out the general opinion of critics. You can use one of two Rotten Tomatoes dataset for this project: the Rotten Tomatoes dataset or Kaggle's dataset .
Access the Sentiment Analysis Project on Movie Reviews with Source Code
3. Analyze IMDb Reviews
The next idea on our list is a machine learning sentiment analysis project. Like Rotten Tomatoes, IMDb is an entertainment review website where people leave their opinions on various movies and TV series. You can perform sentiment analysis on the reviews to find what viewers liked/disliked about the show. This beginner-friendly sentiment analysis project will help you learn about data science and machine learning applications in the entertainment industry.
A movie review generally consists of some common words (articles, prepositions, pronouns, conjunctions, etc.) in any language. These repetitive words are called stopwords that do not add much information to text. NLP libraries like spaCY efficiently remove stopwords from review during text processing. This reduces the size of the dataset and improves multi-class model performance because the data would only contain meaningful words.
These results are useful for production companies to understand why their title succeeded or failed. Beginners can use the small IMDb reviews dataset to test their skills. You can use the IMDb Dataset of 50k movie reviews for an advanced take of the same project.
4. Reviews of Scientific Papers
Sentiment analysis of citation contexts in research/review papers is an unexplored field, primarily because of the existing myth that most research papers have a positive citation. Additionally, negative citations are hardly explicit, and the criticisms are often veiled. There is a lack of explicit sentiment expressions, and it poses a significant challenge for successful polarity identification.
Deriving sentiments from research papers require both fundamental and intricate analysis. In such cases, rule-based analysis can be done using various NLP concepts like Latent Dirichlet Allocation (LDA) to segregate research papers into different classes by understanding the abstracts. LDA models are statistical models that derive mathematical intuition on a set of documents using the ‘topic-model’ concept.
Sentiment analysis of citations in scientific papers and articles is an exciting project idea that can help researchers figure out what experts in their fields think about a topic. Here’s the scientific paper dataset to get started on this project: Machine Learning Dataset . It has N = 405 instances and is stored in JSON format.
5. Track Customer Sentiment Over Time
As the business changes, so do customer interests and sentiments. When businesses start a new product line or change the prices of their products, it will affect customer sentiment. Tracking customer sentiment over time will help you measure and understand it. A change in sentiment score indicates if your changes emotionally resonate with the customers. Tracking both positive and negative sentiments will help companies improve products and fix blunders.
Learners can use open-source libraries like TensorFlow Hub, which can help you perform text-processing on the raw text, like removing punctuations and splitting them into spaces. You can use the deep neural network (DNN) classifier model from the TensorFlow estimator class to better understand customer sentiment. A DNN classifier consists of many layers and perceptrons that propagate for enhancing accuracy.
You can use the Predicting Customer Satisfaction dataset or pick a dataset from data.world .
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6. Customer Feedback Project
The following sentiment analysis example project is gaining insights from customer feedback. If a business offers services and requests users to leave feedback on your forum or email, this project can help determine their satisfaction with your services. It can also determine employees' emotional satisfaction with your company and its processes. Sentiment analysis can read beyond simple sentences and detect sarcasm, read common chat acronyms (LOL, ROFL, etc.), and correct common mistakes like misused and misspelled words.
Understanding sentiments of customer feedback involves text-processing techniques like part-of-speech tagging and lemmatization (transforming a word to its root form). These transformations help developers to clean data for feature engineering . Learners can perform such analysis in a short duration while using fewer resources with cloud services tools like IBM Watson or Amazon Comprehend.
You can use the Customer Feedback Dataset for this project.
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7. Analyze a Company’s Reputation (News + Social Media)
For this intermediate sentiment analysis project, you can pick any company to perform a detailed opinion analysis. Sentiment analysis will help you to understand public opinion on the company and its products.
To find the public opinion on any company, start with collecting data from the relevant sources, like their Facebook and Twitter page. Analyze the conversations between the users to find the overall brand perception in the market. For a more detailed analysis, you can scrape data from various review sites.
Python provides many scraping libraries like ‘Beautiful Soup’ to collect data from websites. This data can then be converted into a dataframe using the Pandas library. To perform NLP operations on a dataframe, the Gensim library can be effectively used to carry out N-gram analysis apart from basic text processing. N-gram analysis helps you to understand the relative meaning by combining two or more words. If two words are combined, it is termed ‘Bi-gram,’ and the connection of three words is called ‘Tri-gram’ analysis. This analysis considers the association of words to understand the actual sentiment of the text. For instance, if Bi-gram analysis is performed on the text “battery performance is not good ,” it will reflect a negative sentiment.
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8. Twitter Sentiment Analysis
In the first advanced sentiment analysis project, you'll learn how to make a Twitter sentiment analysis project using Python. Twitter helps corporations, businesses, and governments to get public opinion on any trending topic. For this Twitter sentiment analysis Python project, you should have some basic or intermediate experience in performing opinion mining.
You must also have some experience with RESTful APIs since Twitter API is required to extract data. The project also uses the Naive Bayes Classifier to classify the data later in the project. It's a time-consuming project but will show your expertise in opinion mining.
Start with getting authorized credentials from Twitter, create the function, and build your first test set using the Twitter API. Unless you know how to use deep learning for non-textual components, they won't affect the polarity of sentiment analysis. Remove duplicate characters and typos since data cleaning is vital to get the best results. Use the Naive Bayes Classifier for analyzing the sentiment. Finally, test your model and see whether it's producing the desired results.
Performing sentiment analysis on tweets is a fantastic way to test your knowledge of this subject. It'll be a great addition to your data science portfolio (or CV) as well.
Access Job Recommendation System Project with Source Code
9. Sentiment Analysis Based on News Topics during COVID-19
It's been over a year since the first lockdown in many countries worldwide because of the COVID-19 pandemic. The pandemic not only endangered our physical health, but the social distancing posed a significant threat to our emotional stability. With a sentiment analysis project on COVID-19 news, you can understand how others responded to the pandemic and misinformation?
The Textblob sentiment analysis for a research project is helpful to explore public sentiments. You can either use Twitter, Facebook, or LinkedIn to gather user-generated content reflecting the public's reactions towards this pandemic. For a more advanced approach, you can compare public opinion from January 2020 to December 2020 and January 2021 to October 2021.
10. Toxic Comments Classification
Everything from forums, blogs, discussion boards, and websites like Wikipedia encourages people to share their knowledge. However, not every user takes part appropriately. Some see these platforms as an avenue to vent their insecurity, rage, and prejudices on social issues, organizations, and the government. Platforms like Wikipedia that run on user-generated content depend on user discussion to curate and approve content. Maintaining positivity requires the community to flag and remove harmful content quickly.
For the next advanced level sentiment analysis project, you can create a classifier model to predict if the input text is inappropriate (toxic). Use the Toxic Comment Classification Challenge dataset for this project.
Over the years, analyses were mostly limited to structured data within organizations. However, companies now realize the benefits of unstructured data for generating insights that could enhance their business operations. Consequently, there is a rising demand for professionals who can person various NLP-based analyses, including sentiment analysis, for assisting companies in making informed decisions. Gaining expertise by performing the above-listed projects can differentiate you in the competitive data science industry, leading to a better job opportunity for your career growth.
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Aspect-based Sentiment Analysis using Dependency Parsing
In this paper, an aspect-based Sentiment Analysis (SA) system for Hindi is presented. The proposed system assigns a separate sentiment towards the different aspects of a sentence as well as it evaluates the overall sentiment expressed in a sentence. In this work, Hindi Dependency Parser (HDP) is used to determine the association between an aspect word and a sentiment word (using Hindi SentiWordNet) and works on the idea that closely connected words come together to express a sentiment about a certain aspect. By generating a dependency graph, the system assigns the sentiment to an aspect having a minimum distance between them and computes the overall polarity of the sentence. The system achieves an accuracy of 83.2% on a corpus of movie reviews and its results are compared with baselines as well as existing works on SA. From the results, it has been observed that the proposed system has the potential to be used in emerging applications like SA of product reviews, social media analysis, etc.
Sentiment Analysis Applied to News from the Brazilian Stock Market
Trg-datt: the target relational graph and double attention network based sentiment analysis and prediction for supporting decision making.
The management of public opinion and the use of big data monitoring to accurately judge and verify all kinds of information are valuable aspects in the enterprise management decision-making process. The sentiment analysis of reviews is a key decision-making tool for e-commerce development. Most existing review sentiment analysis methods involve sequential modeling but do not focus on the semantic relationships. However, Chinese semantics are different from English semantics in terms of the sentence structure. Irrelevant contextual words may be incorrectly identified as cues for sentiment prediction. The influence of the target words in reviews must be considered. Thus, this paper proposes the TRG-DAtt model for sentiment analysis based on target relational graph (TRG) and double attention network (DAtt) to analyze the emotional information to support decision making. First, dependency tree-based TRG is introduced to independently and fully mine the semantic relationships. We redefine and constrain the dependency and use it as the edges to connect the target and context words. Second, we design dependency graph attention network (DGAT) and interactive attention network (IAT) to form the DAtt and obtain the emotional features of the target words and reviews. DGAT models the dependency of the TRG by aggregating the semantic information. Next, the target emotional enhancement features obtained by the DGAT are input to the IAT. The influence of each target word on the review can be obtained through the interaction. Finally, the target emotional enhancement features are weighted by the impact factor to generate the review's emotional features. In this study, extensive experiments were conducted on the car and Meituan review data sets, which contain consumer reviews on cars and stores, respectively. The results demonstrate that the proposed model outperforms the existing models.
A Comprehensive Guideline for Bengali Sentiment Annotation
Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.
Employee Sentiment Analysis Towards Remote Work during COVID-19 Using Twitter Data
Topic modelling and sentiment analysis of global warming tweets.
With the increasing extreme weather events and various disasters, people are paying more attention to environmental issues than ever, particularly global warming. Public debate on it has grown on various platforms, including newspapers and social media. This paper examines the topics and sentiments of the discussion of global warming on Twitter over a span of 18 months using two big data analytics techniques—topic modelling and sentiment analysis. There are seven main topics concerning global warming frequently debated on Twitter: factors causing global warming, consequences of global warming, actions necessary to stop global warming, relations between global warming and Covid-19; global warming’s relation with politics, global warming as a hoax, and global warming as a reality. The sentiment analysis shows that most people express positive emotions about global warming, though the most evoked emotion found across the data is fear, followed by trust. The study provides a general and critical view of the public’s principal concerns and their feelings about global warming on Twitter.
Transparent Aspect-Level Sentiment Analysis Based on Dependency Syntax Analysis and Its Application on COVID-19
Aspect-level sentiment analysis identifies fine-grained emotion for target words. There are three major issues in current models of aspect-level sentiment analysis. First, few models consider the natural language semantic characteristics of the texts. Second, many models consider the location characteristics of the target words, but ignore the relationships among the target words and among the overall sentences. Third, many models lack transparency in data collection, data processing, and results generating in sentiment analysis. In order to resolve these issues, we propose an aspect-level sentiment analysis model that combines a bidirectional Long Short-Term Memory (LSTM) network and a Graph Convolutional Network (GCN) based on Dependency syntax analysis (Bi-LSTM-DGCN). Our model integrates the dependency syntax analysis of the texts, and explicitly considers the natural language semantic characteristics of the texts. It further fuses the target words and overall sentences. Extensive experiments are conducted on four benchmark datasets, i.e., Restaurant14, Laptop, Restaurant16, and Twitter. The experimental results demonstrate that our model outperforms other models like Target-Dependent LSTM (TD-LSTM), Attention-based LSTM with Aspect Embedding (ATAE-LSTM), LSTM+SynATT+TarRep and Convolution over a Dependency Tree (CDT). Our model is further applied to aspect-level sentiment analysis on “government” and “lockdown” of 1,658,250 tweets about “#COVID-19” that we collected from March 1, 2020 to July 1, 2020. The experimental results show that Twitter users’ positive and negative sentiments fluctuated over time. Through the transparency analysis in data collection, data processing, and results generating, we discuss the reasons for the evolution of users’ emotions over time based on the tweets and on our models.
Aspect Based Sentiment Analysis of Unlabeled Reviews Using Linguistic Rule Based LDA
In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.
Aspect Based Sentiment Analysis of Unlabeled Reviews using Linguistic Rule Based LDA
Measuring citizen satisfaction with e-government services by using sentiment analysis technology, export citation format, share document.
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Sentiment analysis research papers, get most trusted sentiment analysis research papers.
Sentiment analysis is a popular subject area that has experienced great growth over the last decade. The subject deals with many feelings, emotions, opinions, and other things about someone. To analyse this, machine learning and natural language algorithms are used.
The sentiment analysis has various use cases including virtual assistants and content moderation. AI models are a great emotion that recognizes opinion and emotion in various industries. Today, we see a growing interest of people in building emotionally intelligent machines. And, similarly, we see the research being performed in natural language processing (NLP). To highlight the work done in this field, many PhD pursuing researchers have to perform deep analysis and write sentiment analysis research papers to earn good grades and to prove their calibre.
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Creating an emotionally intelligent machine is not easy. Sentiment analysis is an important step that comes in while setting this goal. If you are working on paper writing, then you would understand that sentiment analysis papers will strengthen your work understanding in the field.
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