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01_WordCloud.ipynb		01_WordCloud.ipynb
02_DifferentTokenizers.ipynb		02_DifferentTokenizers.ipynb
03_TrendingTopics.ipynb		03_TrendingTopics.ipynb
04_Sentiment_Analysis_Textblob.ipynb		04_Sentiment_Analysis_Textblob.ipynb
06_SMTD_embeddings.ipynb		06_SMTD_embeddings.ipynb
O5_smtd_preprocessing.py		O5_smtd_preprocessing.py
README.md		README.md
TwitterSentiment_2.ipynb		TwitterSentiment_2.ipynb
Twitter_Sentiment_Analysis_2.ipynb		Twitter_Sentiment_Analysis_2.ipynb

README.md

Social Media

To be added

Set of notebooks associated with the chapter.

Create a wordcloud: How to create a word cloud. This is often used to get a quick sense of given text corpus at hand.
Effect of different tokenizers on Social Media Text Data : Here we show how different tokenizers can give different output for the same input text. When dealing with text data from social platforms this can have a huge bearing on the performance of the task. Here, we will be working with 5 different tokenizers, namely:

Trending topics: Find trending topics on Twitter using tweepy
Sentiment Analysis: Basic sentiment analysis using TextBlob
Preprocessing Social Media Text Data: Common functions involved in the pre-processing pipeline for Social Media Text Data.
Text representation of Social Media Text Data: How to use embeddings to represent Social Media Text Data
Sentiment Analysis: Here we use the preprocessing and representation steps learnt before to build a better classifier.

Color figures as requested by the readers.