In the dynamic landscape of today's digital age, the vast amount of textual data generated on online platforms provides a rich source of valuable insights. Understanding the sentiment behind this data is crucial for businesses, organizations, and individuals alike. Sentiment analysis, also known as opinion mining, is the process of extracting and deciphering sentiments expressed in text data to comprehend the emotional tone and subjective opinions of individuals or groups.
Our Sentiment Analysis Project seeks to harness the power of natural language processing and machine learning techniques to analyze and categorize sentiments within textual content. By leveraging advanced algorithms, we aim to provide a comprehensive understanding of public opinions, customer feedback, or social media discourse. This project holds the potential to unveil nuanced insights, helping businesses make informed decisions, enhance customer satisfaction, and gain a competitive edge in an increasingly interconnected and opinion-driven digital world.
- Data collecting
- EDA
- Data preprocessing
- Data modeling
- GridSearchCV and Cross Validation
- Pipeline and web UI
Using text preprocessing techniques (stopwords, lemmatization, stemming,..) and TF-IDF technique to build 3 models: Logistic Regression, Random Forest and Naive Bayes. Besides, we use GridSearchCV and Cross Validation to choose the best parameter for each model and evaluate their effectiveness in sentiment analysis. After completing the model building and evaluation, proceed to attach the entire process to the pipeline for application.
In conclusion, our Sentiment Analysis Project stands at the forefront of leveraging cutting-edge technology to decode the intricate tapestry of human emotions embedded in textual data. As we navigate the realms of natural language processing and machine learning, our project promises a nuanced understanding of sentiments expressed across diverse digital platforms.
Finally, thanks for the contributions of the team members to complete this project. Even though we can made mistakes, this is a project that the whole team tried our best to complete.