json-document-entity-recogonized-sentiment-analyzed
You are given a JSON file (tweets.json) that contains tweets (sentences) along with the name of the author. Objective 1: Get the most frequent entities from the tweets. Objective 2: Find out the sentiment/polarity of each author towards each of the entities. Sample Input: Assume we have only 4 tweets: Tweet1 by Author1: Pink Pearl Apples are tasty but Empire Apples are not. Tweet2 by Author2: Empire Apples are very tasty. Tweet3 by Author3: Pink Pearl Apples are not tasty. Tweet4 by Author1: Pink Pearl Apples smells really good. Sample output: Entities in the topics extracted: Share a CSV with extracted entities and the frequency of the extracted entity from all the tweets in the following format objective1.csv entity frequency Pink Pearl Apples 2 Empire Apples 2 Sentiment/polarity of Authors: Share a CSV file with predicted sentiment values with extracted entities as columns and unique authors as rows. See the example CSV below.
objective2.csv entity author_name overall_polarity Pink Pearl Apples Author1 Positive Empire Apples Author1 Negative Empire Apples Author2 Positive Pink Pearl Apples Author3 Negative