- 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.
- 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.
- 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.
- 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?
- 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.