Social-Media-Monitoring

Social Media Monitoring /Twitter/ in python language.

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Index

1. About the project
2. Requirements
3. Social Media Monitoring Steps
4. Data Processing
5. Sentiment Analysis with RNN

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About the project

The main goal of this project is to scrape data from Twitter for a specific brand or product and try to classify the tweets sentimatically as negative or positive tweets.

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Requirements

Here is a list of some of the used libraries:

1. d2l: for building the sentiment analysis model.
2. demoji: for replacing the emojis in tweets with their description.
3. snscrape: a library to scrap tweets.
4. Nltk: for natural language processing.

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Social Media Monitoring Steps

In the following figure we can see the main steps of the project: First of all we should select a string to search for, then we need to scrape Twitter for posts containing that string. After that, the data will be cleaned, analyzed and presented in a way to show what sentiments are included in the tweets.

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Data Processing

After scrapping data from Twitter. The following preprocessing pipeline will be apllied :

1. Cleaning signs:

• Replacing emojis with their description.
• Data collected the web and specially from social media platform contain links and signs like (#) and (@)., we remove those signs.
• It is important to delete the links, since no sentiments can be discovered from the link text.
• Deleting punctuation marks and any non-Asci chars.

2. NLP cleaning:

• We keep only English texts.
• Remove all the occurrences of the word that we have searched for.
• The names will be kept and other tokens will be transformed to their lemmas. The Data processing pipeline is shown in the following figure:

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Sentiment Analysis with RNN

The idea is to represent each token using the pretrained GloVe model, and feed these token representations into a multilayer bidirectional RNN to obtain the text sequence representation, which will be transformed into sentiment analysis outputs.

The sentiment analysis model is inspired from: https://d2l.ai/chapter_natural-language-processing-applications/sentiment-analysis-rnn.html