Social Sentiments Case Study

This equity research firm is comprised of several equity research analysts covering multiple industries and sectors. The reports created by firm are widely circulated to the clients and subscribers. Given the increase in social media activity, the research firm decided to include social media sentiment analysis for each equity on their website.

Data & Analysis
We analysed the social media feeds from twitter to better understand what kind of tweets are being produced in relation to each equity. The analysis helped us understand who are the influencers? what positive and negative words are being used? what is the frequency of the tweets? what time of the day is the highest activity?

The model used API twitter to search for tweets related to each equity, the feed was then ingested into a corpus and text analytics was performed. The text analytics model classified each tweet as positive or negative sentiment. A summary level twitter sentiment score was presented.

This text analytics model enhanced the equity reports by providing clients and subscribers with additional level of information that is being expressed by social media as it relates to the equities of interest.