Discovering Binge Watching and Audience Engagement Through Sentiment Analysis

International Journal of Advanced Science and Technology Vol. 29, No. 7, (2020), pp. 8030-8038 Discovering Binge watching and Audience Engagement through Sentiment Analysis

*Devesh Lowe1, Bhavna Galhotra2, Yukti Ahuja3 1,2IT Department, JIMS Rohini Sector-5, Delhi-1100851 *[email protected], [email protected] 3Management Department, JIMS Rohini Sector-5, Delhi-1100852 [email protected]

Abstract Indian audience have trusted and enjoyed television entertainment for the longest time until an alternate medium called Internet based Video on demand (VOD) emerged. The new digital medium let users handpick and watch/listen to video or audio content at a time of their choice. Binge-watching, meaning watching multiple episodes of the same TV series continuously constitutes this phenomenon. Little is known about the transition in the Indian viewer’s behaviour from scheduled broadcast viewership to marathon consumption of entertainment. This study explores how social platforms are deployed to reach and entice audience towards Netflix, Amazon Mx player etc. The study unfolds the audience sentiments towards OVD programming, its promotion and impact with the use of sentiment analysis. The methodology deployed for the study includes a preliminary analysis of secondary data comprising articles, research papers and cases followed by analysis of data collated via Twitter to articulate the viewers’ opinion. The results of the study reflect upon the rising interest in web content and growing fondness for web based video programming. The study is first of its kind in the Indian context and has key takeaways for practitioners in the VOD programming industry, marketing professionals and researchers working in this arena. Keywords: Internet Based Video Program, Binge Watching, Social Media, Twitter Sentiment Analysis.

1 Introduction:

With constant advancements in the technology, viewers consume entertainment content available on various platforms. Binge watching has acquired much limelight [1]. There is a sea change in the landscape of the television industry with the availability of the internet services, the streaming services on television and also the entry of the OTT platforms like Netflix, Amazon Prime and Voot etc. which entered with original content all at once (Devesh Lowe et al., 2019). Binge watching is supported by many other factors like-

Binge Dumps: The availability of all the episodes of the show at the launch itself either on the OTT platforms or television networks [1]

TV Everywhere: Availability of the services provided by the service providers, which lets the user access the services anywhere and anytime. This is an on demand service provided to the users by downloading the respective applications and sign in with the superscription based plans [2]

Bandwidth: Rate of transferring the data in a point of time i.e. how much data can be downloaded and how easily the user can watch or get connected with the content available.

International Journal of Advanced Science and Technology Vol. 29, No. 7, (2020), pp. 8030-8038

Over the top: Service based subscription that needs to be opted by the user to check and view the content of his/her own choice , it can be easily consumed in a digital context via wired or wireless network.

Streaming Services:Video on demand services which are different from the traditional source of watching television and are available through the cable networks or satellite media.

Cord cutting: Process of cutting more expensive cable packages in order to change to a low-cost television subscription through over the-air (OTA), where the free broadcasting is possible over the internet anywhere and under all conditions. This is growing up fast and having an adverse effects on the cable and other digital industries. In India Netflix, Amazon Prime, Voot, Hotstaretc some of the popular subscription services that encourage cord cutting

Pay-Tv:Aservice which is provided on the basis of a subscription fee for a stipulated period of time. Users are required to login with the unique user id and password and then they are free to watch the content available. Also there are various service providers that take note of the IP addresses and do not allow more that 3 IP address to be logged in for the content available.

In the present study authors have tried to investigate the link between Binge watching and twitter as a tool to promote it. It is observed that OTT companies are promoting their video content using various tools available on twitter. Effective use of social media not only creates a buzz in audience but also acts as an important factor to engage them for the upcoming series. In section II authors discuss the promotional campaigns that are launched using Twitter. In section III, authors present the complete process of fetching and analysing the data from twitter developer accounts based on popular hashtags, twitter handles, Tweets, retweets, likes and dislikes. In the process we have recorded certain observations which establish a direct relationship between binge watching and its social media connection.

2 Twitter: A Promotional Tool

Twitter is a micro blogging site where users from all walks of society voice their opinion about any topic of their choice [3]. Twitter started in 2006 in California, USA and is now world‟s leading news and social networking service. Active users of twitter, commonly called twitteratis often reflect the opinion of society on a large about various current affairs. A tweet can be published in a word limit of 280 words and can reflect a person‟s opinion which he wants to share with his followers or any onlooker. Every user of twitter uses a special identification called twitter handle which is a unique username prefixed with „@‟ sign. Tweets can be written with special reference to any other twitter handle or by using some recognizable keywords called „hashtags‟.

It is considered to be an integral part of their digital strategy by most large corporations; customers also use twitter to actively voice their opinions regarding products and services. Netflix is the most popular online streaming service that lets customers watch a wide variety of award-winning TV shows, movies, documentaries without any commercials. PrashantSawant elaborates upon use of Twitter to forecast popularity and sales revenue of electronic products [4]. From many case studies, we can know that Twitter is really useful for predicting products, services, or markets.

Development Twitter explains the various ways how it is considered as a platform [5] for many promotional activities also these are used in launching and promoting their web contents

Advertise: Programmatically create and manage twitter advertisement campaigns, Once Twitter Ads campaigns have been set up and launched and there will be attention of the users, the immediate step is to analyse the performance of these campaigns by checking the likes and retweets. The performance of the

International Journal of Advanced Science and Technology Vol. 29, No. 7, (2020), pp. 8030-8038

campaign can be measured through engagements, impressions, retweets, likes, and several others. Before the launch of the web series the OTT channel may create a handle to advertise their upcoming series where they can easily see the response of the viewers or may just observe the responses published on twitter.

Publish and curate: The step in which the promoters of the web series create a content/story which is directly or indirectly linked with the series. Then they configure timelines to automatically display the live updates from trends, people, and places them in the application for or by the publisher tools available with twitter to engage with audience. Twitter also provides a suite of tools to integrate Twitter content and features in websites and applications.

Analyse: Data from twitter presents an inside story of the audience, market requirements, emerging trends, expectations etc. Sentiment analysis can predict the outcome of upcoming events, evaluate the impact of a recent launch, pivot the direction or content of an ad campaign, and more.

Engage: Twitter is the best place in the world for businesses and people to connect [5]. Since the launch of the season till its availability people and promoters use twitter to connect with audience and also for conversational nature of the platform to engage with businesses. The public, live, and conversational nature of Twitter had made it an ideal place for people to engage with the upcoming web series which has created a large amount of people to turn to Twitter when they want to connect with a business.

3 Methodology

In order to study the promotions and audience perceptions towards OVD, authors used twitter for the purpose of collection of data. Authors observed that OVD, binge watching and subscription based on-line viewing is hugely popular amongst twitteratis. Every launch of new web-series receives thousands of tweets with every emotion possible. Authors compiled a list of original web-series content provided by most popular online content providers namely Netflix, MXPlayer and Amazon Prime Videos. For the purpose of current study. Authors tried to explore the huge resources of twitter by extracting tweets related to original content hosted on Netflix, MXPlayer and Amazon Prime, as tweeted by common users and media promoters. Authors used their developer‟s accounts at twiiter.com and python scripts to extract tweets related to the presented study. Authors used the wide range of libraries available with python like numpy, tweepy, textblob and pandas for the purpose of data collection, data cleansing, data storage and sentiment analysis.

Python scripts written using IDEs like Spyder and Anaconda were used to extract data from twitter using tweepy. To avoid any misuse of downloaded data, twitter ensures that data is extracted by authentic users who have verified consumer_key, consumer_secret_key, access_token and access_secret key. This verification is done using following authentication method call of tweepy:

auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)

auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)

api=tweepy.API(auth)

After authentication, data was extracted using search method as under:

tweets = api.search(a, count = count)

International Journal of Advanced Science and Technology Vol. 29, No. 7, (2020), pp. 8030-8038

Above command was repeatedly performed using iterative procedure to extract maximum tweets by setting the upper limit to upto 300 tweets in certain cases. Also to be noted that while performing search, authors focussed not only on the official twitter handles of the content broadcasters but also focussed on public tweets using the random hashtags used by twitteratis. Data hence collected was cleaned of their weblinks, Retweets(RTs) tags etc to avoid duplicity. This process involved use of re and numpy. Numpy library was used to extract separate information from tweets like, date and time of tweet, user_id, source, length of tweet, number of likes on each tweet and number of Retweets that were recorded on each tweet. All data hence cleaned and formatted was stored in dataframes of pandas (python data analysis) object for sentiment analysis and was also stored in separate comma separated spreadsheets for future use. Sentiment analysis was done using TextBloba powerful library of python. For sentiment analysis authors used polarity property of textblob which indicate the sentiment expressed in tweet by user by the choice of words.

analysis = TextBlob(clean_tweet(tweet))

ifanalysis.sentiment.polarity> 0:

return 1

elifanalysis.sentiment.polarity == 0:

return 0

else:

return -1

Any use of positive word will make the polarity positive (1), use of negative words will make the value of polarity negative (-1) and neutral tweets make polarity neutral (0). Using regular expressions library, a summarized percentage of every sentiment expressed in tweets was recorded. All the collected data was summarized using MS-Excel to prepare final observations.

4 Analysis and Observations:

With the data collected from twitter and original content hosted on Netflix, MXPlayer and Amazon prime videos, authors recorded some observations as discussed in this section. Authors have already established in their previous work that online-video content hosted on Web is hugely popular in India (Lowe et al.2019) and has established itself as a competitor for conventional satellite /cable television. To record the effective use of twitter by above mentioned content providers and common users, we prepared a list of all original series webcasted in a timeline of January 2018 to February 2019 (table 1). On the basis of data collected we recorded following observations:

a. Social Media As A Tool Of Marketing-

Authors observed that not many web content providers have taken social media marketing as a tool for promotion of their original content. Though content hosting sites Amazon, Netflix and MXPlayer are using their own twitter handles through which they are regularly updating their followers and visitors about current content, upcoming series and try to sensationalize content to raise awareness, but majority of individual web-series production houses are still not focussing on this platform. We observed that most of web-series on Amazon were promoted through individual twitter handles like @BreatheAmazon,

International Journal of Advanced Science and Technology Vol. 29, No. 7, (2020), pp. 8030-8038

@comicstaan and @YehHaiMirzapur with hundreds of tweets to their credit. Tweeting about an event, an episode or a special reference to any character of web-series creates a buzz in the social media and the posted content is immediately retweeted or shared on even other media platforms. Content of MXPlayer and Netflix are relatively new in this domain. Their promotion strategy doesn‟t include promotion through twitter. When official twitter handles of comicstaan and Breathe collectively posted more than 6000 tweets (refer table 3) in last one year, contribution of web-series of Netflix was nil.

b. Understanding the importance of own twitter handle as compared to hashtags:

Our data shows that user likes to post immediate comments whether positive or negative on the social platform. While doing this a user commonly has the intention to tag a person through a twitter handle. This issues an alert notification to the desired twitter handle. We observed that this is an important and most authentic way to collect feedback from directly your audience about the content hosted. Moreover it‟s a unique way to connect with your customer directly and even respond to the query or show your gratitude for positive feedback. In modern era this is considered as most effective way to move into a relationship with your audience. Our data shows that most production houses are still not using their own twitter handles and are relying on other hashtags or in most cases not using any online promotion.

c. Frequency of Retweets and Likes for increased visibility:

Using twitter is not only for tweeting or posting content. Today these microblogging sites are used for interaction and response. Its role is majorly into increasing the visibility and reoccurrences. We observed that some web-series content was heavily promoted. For example „made in heaven‟ on Amazon was promoted with more than 66 tweets in 4 weeks which received appx 40,000 Likes from twitteratis (ref table 3). Audience also responded (ref table 2) with more than 6000 tweets 18,548 Likes and 1,69,950retweeted. Tweets, retweets, like etc were going on for regular intervals. Immediate responses to audience, liking their comments, responding to feedback and retweeting any tweet increase the visibility of content repeatedly on social platforms.

d. Positivity in content is reflected in mood of the twitterati:

Authors observed that the sentiment towards web content amongst twitterati is hugely positive (ref table 4). Considering the Indian perspective and the uncontrolled, uncensored viewing of video content amongst users, we found that Indian audience is receiving this new type of video content very well. Table 4 reflects the positivity in tweets posted by common audience. A clear mood swing is reflected when we see that selection of words for posting feedback in tweets is below 15% in most cases and 10% average. This reflects the mood of the audience and how well they receive this new age content.

e. Support for video content across all genre:

It was also observed that Indian audience is well receiving video on-demand content of all genre (ref table 1,2,3,4). It is also worth tweets from audience towards family drama series and reality game shows is way higher than mature content. Highest response from audience was for comedy reality show @comicstaan, thriller drama series @BreatheAmazon and newly webcasted family drama @madeinheavenTV. Whereas web-series providing mature content like #immature, #mirzapur and #sacredGames received lesser response from audience.

International Journal of Advanced Science and Technology Vol. 29, No. 7, (2020), pp. 8030-8038

Table 1:popular original content series with twitter handles

launc h twitter handle twitter handle Series name Portal date host series popular #tags

#breathe, Jan- @amazonprim @BreatheAmaz @actorMadhavan, Breathe Amazon 18 eIN on @theAmitSadh

Chachavidhayakhainha May- @amazonprim mare Amazon 18 eIN NA NA

Jul- @amazonprim Comisctaan Amazon 18 eIN @comicstaan #comicstaan

Sep- @amazonprim @HearMeLove hear me love me Amazon 18 eIN Me_ #hearmeloveme

Nov- @amazonprim @YehHaiMirza #mirzapur, Mirzapur Amazon 18 eIN pur #pankajtripathi

Jan- @amazonprim #fourmoreshotspleas Four More shots please Amazon 19 eIN @4moreshotspls e

Mar- @amazonprim @madeinheaven Made in Heaven Amazon 19 eIN tv #madeinheaven

Jul- @sacredgames_ Sacred Games Netflix 18 @netflixIndia TV #sacredgames

Aug- Ghoul Netflix 18 @netflixIndia NA NA

Dec- Selection Day Netflix 18 @netflixIndia NA NA

MXPlay Aafat er 2019 @MXPlayer NA #aafat

MXPlay IamMature er 2019 @MXPlayer NA #IAmMature

International Journal of Advanced Science and Technology Vol. 29, No. 7, (2020), pp. 8030-8038

MXPlay Hey Prabhu er 2019 @MXPlayer NA #heyPrabhu

MXPlay Apharan er 2018 @MXPlayer NA #apharan

Table 2: Viewers tweets data using hashtags from 7/3/19 to 23/3/19

Tweets Likes RTs Positivity Neutral

AAFAT 100 322 552 39 51

Made in Heaven 6000 18548 169950 54 33

ImMature 730 1486 1228 38 44

Hay Prabhu 204 320 281 59 32

Four More Shots Please 279 392 2618 58 33

Mirzapur 1136 2201 6837 26 72

Table 3:Official Tweets by content providers using twitter handle and their repsonse

Tweets Likes RTs Positivity Neutral

Made in Heaven 66 39906 2556 41 55

Four More shots please 56 38908 2944 41 55

comisctaan 3211 20324 1621 79 20

Mirzapur 173 85828 7182 5 93

Breathe 3144 6004 1078 28 72

hear me love me 43 542 70 63 21

Sacred Games 50 17060 3902 26 56

International Journal of Advanced Science and Technology Vol. 29, No. 7, (2020), pp. 8030-8038

Table 4: Positivity in tweets by common audience

Positivity Neutral Negative

AAFAT 39 51 10

Made in Heaven 54 33 13

ImMature 38 44 18

Hay Prabhu 59 32 9

Four More Shots Please 58 33 9

Mirzapur 26 72 3

comisctaan 79 20 1

hear me love me 63 21 16

Average 52 38 10

5 Conclusion:

In the present study authors have tried to establish a relationship between online social media platforms and web-casted video content. We have presented the analysis done by us on the data collected using popular social media platform „twitter‟. We have tried to present a direct connection between audience and content provider in form of social media. If used effectively, this platform can be used as an effective tool for creating awareness about online content. Also to note that it is the most authentic way to connect with the consumer. Our data collected in the form of more than 15,000 tweets not only reflect the mood of the audience but also indicate the effectiveness with which some promoters are using social media.

6 Limitations and Future Work

Presented study is an attempt to analyse the use of social media by web-content providers for marketing and creating a buzz around audience. In the present work authors have relied on only one social media platform i.e twitter. In our subsequent work, we intend to use other platforms like Facebook and Youtube for studying the entertainment consumption pattern by Indian audience. Another limitation is the use of predefined analytics libraries of Python. We propose to create word libraries for sentiment analysis which should focus more on words which are commonly used by Indian audience preferably in local languages but written in English.

International Journal of Advanced Science and Technology Vol. 29, No. 7, (2020), pp. 8030-8038

References:

1. R. Raikar, The Binge Watching Trend: An Analysis of Audience Behavior and Network, Drexel University, 2017. 2. “PCmag.com,” [Online]. 3. A. K. K. Rucha Jadhavar, “Sentiment Analysis of Netflix and Competitor Tweets to Classify Customer,” sas, 2018. 4. A. V. P. G. B. Ankit Pradeep Patel, “Literature Survey on Sentiment Analysis of Twitter Data using Machine Learning Approaches,” IJIRST –International Journal for Innovative Research in Science & Technology, vol. 3, no. 10, March 2017. 5. “https://developer.twitter.com,” [Online]. 6. A. K. a. T. M. Sebastian, “Sentiment Analysis on Twitter,” IJCSI International Journal of Computer Science, vol. 9, no. 4, 2012. 7. B. G. Y. A. Devesh Lowe, “Unfurling the Latest Patterns of Entertainment Consumption by Indian Audience: A Twitter Sentiment Analysis,” IJRTE, Vol 7, issue 6C, pp. 135-139, 2019.