Twitter Sentiment Analysis(TSA) - Online Retailers(2019-2021)

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Project Overview:

Twitter Sentiment Analysis helps to understand the user experience of the online retailers based on the tweets, which is important for business development and product enhancement.This a online retailer problem so based on the tweets we are giving the rating of each customer about online shopping experience.

Sentiment analysis architecture: 



Approach for Twitter sentiment analysis of online retailers:

Scraping the tweets:

  1. Taking the tweets with respect to online retailers such as ebay, meesho, olx india, flipkart, amazon, jio mart. 
  2. After the extraction of the data from twitter, put into the data frame with respect to retailers. 
  3. Concatenating the data frame of each retailer into a single data frame. 
  4. Converting the data frame to CSV file. 

Data preparation and data cleaning:

  1. Importing the CSV file using pandas.
  2. Analyse & understand the nature of the data. 
  3. Tweets are not actual text format, it contains no text characters, writing a function to clean that and passing into the tweets data frame.

Modelling:

  1. Once the data is cleaned, we will use the hugging face nlp model to predict the sentiment of tweets and give the rating based on the expression. 
  2. Writing a function to apply the nlp model. 
  3. Creating a new column and apply nlp function to data frame and finding the rating 
  4. Removing the null value in the tweet column

Data visualisation:

  1. After finding the sentiment, count the rating of sentiment.
  2. Visualisualize the sentiment using Matplotlib.


Conclusion: 

From the fig, we conclude that most people usually prefer to buy online. This is due to Covid-19 and increasing mobile application & best user interface of some online retailer web and mobile applications. Enhancing the customer support of the online retailers helps in better production and sales. 

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