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Applying Sentiment Analysis to Star Wars: The Force Awakens

One of the more influential sites for data scientists, KDNuggets recently published a case study showing how sentiment analysis could be applied to track the reaction around a film’s early release cycle.  In this case, the film was the 2015 holiday blockbuster Star Wars: The Force Awakens.

10 milliostarwarsSA-1n tweets were collected through the Twitter API, between 12/4/15 and 12/29/15, with the release date on 12/17/15.  About 2.5% contained geolocation data either in form of direct coordinates or human readable location (e.g. New York). The researchers said “…the first thing we looked at was the frequency of Star Wars related tweets in time. It is clearly visible that most of the tweets came from US and UK, which can be easily explained by popularity of Twitter itself in these countries. Next thing to see is the periodicity of day and night, where people tweet more at night than during the day. Also the timezone shift is clearly visible.  More interestingly, we can see the build up before the release, as the number of tweets is increasing for a few days before the world premiere and sky rocketing on this day…”

starwarsSA-2Each tweet was assigned a score between -1 and +1 (-1 being highly negative, +1 highly positive). Results were plotted in a hexbin map, visualizing global sentiment and aggregating by mean within the cell.  Interestingly, average sentiment shows a steady decline as the time passes. There is an observable dip on the day of world premiere but “sentiments keep steadily low the whole time.” The researchers make several interesting observations concerning the results.  Since worldwide interest in the film, at least as reported in the media, approached general hysteria, why doesn’t the Twitter analysis parallel this?

One possible explanation is the inherent sampling bias when working with social network data.  After all, data is derived only from those who voluntarily decide to share. These are usually the ones with stronger opinions – either highly positive or negative, producing a somewhat polarizing effect.  Next,  sentiment analysis is constrained by the modeling methods and tools available for Natural Language Processing (NLP), and one of these constraints is that the algorithms require a data corpus in the English language.  Sentiment analysis that proposes a global sampling plan will necessarily have gaps in its dataset, since non-English texts will be omitted from the analysis.