Title: Sentiment Analysis and Wordcloud Twitter Data

[SKG24]

Initiative (Required)

GSSoC (Girl Script Summer of Code) 🌸

Is your feature request related to a problem? Please describe.

The objective of this code is to perform sentiment analysis on Twitter data related to Apple, represented by tweets in a CSV file. The tasks involve reading, cleaning, and structuring the textual data for processing, and then extracting meaningful insights. This involves creating a word cloud to visualize the most frequent words, building a Term Document Matrix (TDM) to quantify word occurrences, and performing sentiment analysis to categorize the emotional tone of each tweet. By identifying and analyzing sentiments like anger, joy, and sadness, as well as visualizing word frequencies, the analysis aims to understand the general public sentiment towards Apple as conveyed on Twitter.

Describe the solution you'd like.

The code starts by reading in the Twitter data, creating a "corpus" (text collection) for text mining, and performing a series of cleaning operations—converting text to lowercase, removing punctuation, numbers, URLs, stopwords, and irrelevant terms specific to Apple, like "AAPL." A Term Document Matrix is then created, enabling structured analysis of word frequencies. Using the wordcloud package, a word cloud is generated to visualize the most frequently used words. For sentiment analysis, the syuzhet package is utilized, which applies the NRC Emotion Lexicon to assign sentiment scores to each tweet, classifying them into emotions such as anger and joy. The results are visualized in a bar plot, showing the prevalence of each sentiment in the dataset, thus helping to derive a comprehensive view of public opinion around Apple on Twitter.

Add any other context or screenshots about the feature request here.

[github-actions]

@SKG24

It's great having you contribute to this project

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[github-actions]

Hi @SKG24 👋, your issue #1626 has been successfully closed ✅. Thank you for your valuable contribution! 🙌