Sentiment Analysis Machine Learning Projects

Sentiment analysis is also called opinion mining that includes examining and retrieving the sentiment like positive, negative, neutral and even more fine-grained measures from text data. We go through current years journal like IEEE and suggest valuable topic as per you brief. Here are various project ideas that we propose in the real-time sentiment analysis using machine learning (ML):

  1. Product Review Observations:
  • Goal: We categorize product ratings into positive, negative and neutral from commercial business sites.
  • Data Source: Amazon product feedbacks, Yelp restaurant reviews are useful to us.
  • Tools: Scikit-learn, TensorFlow, PyTorch and BERT are the tools that help our project.
  1. Movie Feedback:
  • Goal: Our project detects the sentiment of film ratings.
  • Data Source: IMDB movies feedback dataset is supporting us.
  • Tools: NLTK, Scikit-learn, RNN and LSTM are techniques we employ in this project.
  1. Real-time Twitter:
  • Goal: We measure public sentiment on certain titles and hash tags in real-time scenarios.
  • Data Source: Twitter API is beneficial to our project.
  • Tools: Tweepy, TextBlob and BERT are the models that assist us.
  1. News:
  • Goal: To interpret the common state of significant activities and objects we examine the sentiment of news articles.
  • Data Source: Web scraping and online news portal are required in our project.
  • Tools: BeautifulSoup and Scikit-learn serve us in this work.
  1. Customer support:
  • Goal: For making preferences in major problems we categorize client support questions.
  • Data Source: Emails and customer support chats offer valuable insights for us.
  • Tools: Scikit-learn, TensorFlow and spaCy are techniques that help our project.
  1. Sentiment analysis for Multilingual:
  • Goal: We observe sentiments in many languages.
  • Data Source: Item reviews, tweets, others in different languages are useful to us.
  • Tools: Multilingual BERT (mBERT) and FastText help our project.
  1. Emotion Detection in Text:
  • Goal: Our work detects particular emotions such as happiness, sadness, anger, etc. rather than basic sentiment.
  • Data Source: Blogs, diaries and surveys are data sources we implement in this project.
  • Tools: Scikit-learn, RNN, LSTM are the approaches that assist us.
  1. Visualizing Sentiments on a Geographical Map:
  • Goal: Depending on the area, we display the sentiments towards a specific title using geotagged data.
  • Data Source: Geotagged tweets and posts are essential for our project.
  • Tools: The platforms like Tweepy, TextBlob and Folium help us.
  1. Contextual Embedding:
  • Goal: To capture better context in sentiment analysis, incorporating deep learning structures in our project.
  • Data Source: Here we get all textual data with sentiment labels.
  • Tools: BERT, RoBERTa, GPT-2 are the tools we utilize for this research.
  1. Aspect-based Sentiment Analysis:
  • Goal: We understand sentiment towards certain aspects of a product and service. For example, the battery life of this phone is excellent but its camera is not good.
  • Data Source: Product feedback is valuable in our work.
  • Tools: BERT, spaCy and dependency parsing are the methods applicable for us.

It is crucial to pre-process the text data for these projects. This includes tasks such as eliminating stop-words, stemming or lemmatizing and tokenization, etc. Based on our issue’s difficulty we begin with easy ML frameworks such as Naive Bayes and Logistic Regression then go to complicated models such as LSTMs and transformer-based structures like BERT.

Libraries such as Matplotlib, Seaborn and other communicative techniques like Plotly are helpful to visualize the outcomes in our project. Finally, make sure that we contain appropriate training or test segments and validate our system’s efficiency by employing methods such as cross-validation.

Sentiment Analysis Machine Learning Topics

Sentiment Analysis Machine Learning Thesis Ideas

Go through our recent Sentiment Analysis Machine Learning thesis topics that our professionals have developed ,share with our faculty members about your research issues we will make use of latest tools and offer best solutions so that you score high grade.

  1. State of the art: a review of sentiment analysis based on sequential transfer learning
  2. Sentiment analysis in education research: a review of journal publications
  3. Survey on sentiment analysis: evolution of research methods and topics
  4. Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research
  5. Research on non-dependent aspect-level sentiment analysis
  6. Multimodal sentiment analysis based on fusion methods: A survey
  7. More than a Feeling: Accuracy and Application of Sentiment Analysis
  8. A large scale group decision making system based on sentiment analysis cluster
  9. Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis
  10. Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews
  11. A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research
  12. A survey on sentiment analysis and its applications
  13. Aspect-based sentiment analysis: an overview in the use of Arabic language
  14. Improving aspect-based sentiment analysis with Knowledge-aware Dependency Graph Network
  15. A Lexicon Enhanced Collaborative Network for targeted financial sentiment analysis
  16. Aspect-level sentiment analysis: A survey of graph convolutional network methods
  17. Back to common sense: Oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis
  18. Machine learning and deep learning for sentiment analysis across languages: A survey
  19. Evaluating new energy vehicles by picture fuzzy sets based on sentiment analysis from online reviews
  20. Using sentiment analysis to evaluate qualitative students’ responses
  21. Sentiment analysis and text categorization of cancer medical records with LSTM
  22. Natural Language Processing Implementation for Sentiment Analysis on Tweets
  23. A BERT Framework to Sentiment Analysis of Tweets
  24. An improved model for sentiment analysis on luxury hotel review
  25. Arabic Tweets-Based Sentiment Analysis to Investigate the Impact of COVID-19 in KSA: A Deep Learning Approach
  26. Ensemble Stacking Model for Sentiment Analysis of Emirati and Arabic Dialects
  27. Sentiment analysis: A survey on design framework, applications and future scopes
  28. The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model
  29. Bayesian game model based unsupervised sentiment analysis of product reviews
  30. Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network
  31. A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis
  32. Sentiment analysis for customer review: Case study of Traveloka
  33. Travelers’ online review on hotel performance – Analyzing facts with the Theory of Lodging and sentiment analysis
  34. KSCB: a novel unsupervised method for text sentiment analysis
  35. Unexpected surprise: Emotion analysis and aspect based sentiment analysis (ABSA) of user generated comments to study behavioral intentions of tourists
  36. Integrating external knowledge into aspect-based sentiment analysis using graph neural network
  37. Survey on aspect detection for aspect-based sentiment analysis
  38. Explainable hybrid word representations for sentiment analysis of financial news
  39. SpSAN: Sparse self-attentive network-based aspect-aware model for sentiment analysis
  40. Managing demand volatility of pharmaceutical products in times of disruption through news sentiment analysis