• Positive sentiment for Swiggy as compared to Zomato.
• Identification of crucial Factors for selection of food delivery app. • Sentiment Analysis. • Recommendations and Suggestions
• Collected around 50K tweets from official Swiggy and Zomato handles
• Word Cloud: We created word cloud for both Swiggy and Zomato reviews to identify the critical factors for choosing the service delivery application. Both the word cloud hinted at "Restaurant", “Refund” “Money” and "Waiting" as the most repeated words.
• Bar Charts: We draw Bar charts to quantify/verify the word repetition count and have the similar result what we get in word cloud.
• Co-Occurrence Graphs: connects those tokens together that most co-occur within the document, using a network graph wherein the nodes are the tokens of interest. They help us identify what are the most closely connected issue related to the key factors we have identified. They also give us some hint about the sentiment of the customers towards the factors identified.
• Sentiment Analysis through Valence Shifters: We did sentiment analysis through valence shifters and words. We want to see which word contribute most to positive and negative sentiment in the corpus using bing lexicon.
• Improvement in App environment • Settlement of third-Party issues
• We can extend this project by including more number of tweets for sentiment analysis. • We can also include demographic study for geographical locations and time zones.