Twitter-Sentiment-Analysis

Introduction

• Natural Language Processing (NLP) is a prominent area of research in data science, with sentiment analysis being one of its common applications.
• Sentiment analysis has revolutionized business operations, impacting areas like opinion polls and marketing strategies.
• NLP enables the rapid processing of large text datasets, saving time compared to manual analysis.

Understand the Problem Statement

• The objective is to detect hate speech in tweets, classifying them as racist/sexist (label '1') or non-racist/sexist (label '0').
• The evaluation metric for this task is the F1-Score.

Tweets Preprocessing and Cleaning

• Preprocessing of text data is crucial to ready it for mining and applying machine learning algorithms.
• Data cleaning involves structuring the data, similar to organizing items in an office space for easy access.
• The objective is to remove noise, such as punctuation, special characters, numbers, and less relevant terms, from the text.
• Proper data preprocessing results in a better quality feature space when extracting numeric features.

Story Generation and Visualization from Tweets

• Exploring and visualizing cleaned tweets is vital for gaining insights.
• Common questions to consider during exploration:
  • What are the most common words in the entire dataset?
  • What are the most common words in negative and positive tweets?
  • How many hashtags are there in a tweet?
  • Which trends are associated with the dataset and the sentiments?

Conclusion

• The sentiment analysis approach involved preprocessing, data exploration, and feature extraction using Bag-of-Words and TF-IDF.
• Models were built using these feature sets to classify tweets.
• Readers are encouraged to share their experiences and discuss additional methods for feature extraction in the comments or discussion portal.