Conference PaperPDF Available

SentiTVchat: Sensing the mood of social-TV viewers

Authors:

Abstract and Figures

In this paper, we propose a novel Social-TV chat system integrating new methods of measuring TV viewers' feedback and new multi-screen interaction paradigms. Viewers' messages in chat services are a valuable asset for their peers in general, and for the cable TV operator in particular. The proposed system analyses chat-messages to detect the mood of viewers towards a given show (i.e., positive vs negative). This data is plotted on the screen to inform the viewer about the show popularity. Although the system provides a one-user / two-screens interaction approach, the chat privacy is assured by discriminating information sent to the shared screen or the personal screen. We evaluated the system on a first experiment with labeled data to assess the accuracy (78%) of the chat analysis algorithm and a second experiment with live chat data to validate the user interface.
Content may be subject to copyright.
SentiTVchat: Sensing the Mood of Social-TV Viewers
Flávio Martins, Filipa Peleja, João Magalhães
Dep.Informática, Faculdade de Ciências e Tecnologia,
Universidade Nova de Lisboa, Portugal
flaviomartins@acm.org, filipapeleja@gmail.com, jm.magalhaes@fct.unl.pt
ABSTRACT
In this paper, we propose a novel Social-TV chat system
integrating new methods of measuring TV viewers’ feedback and
new multi-screen interaction paradigms. Viewers’ messages in
chat services are a valuable asset for their peers in general, and for
the cable TV operator in particular. The proposed system analyses
chat-messages to detect the mood of viewers towards a given
show (i.e., positive vs negative). This data is plotted on the screen
to inform the viewer about the show popularity. Although the
system provides a one-user / two-screens interaction approach, the
chat privacy is assured by discriminating information sent to the
shared screen or the personal screen. We evaluated the system on
a first experiment with labeled data to assess the accuracy (78%)
of the chat analysis algorithm and a second experiment with live
chat data to validate the user interface.
Categories and Subject Descriptors
H.5.1 [Multimedia Information Systems]
General Terms
Design, Experimentation, Human Factors.
Keywords
SocialTV, chat, sentiment analysis, multi-screen interaction.
1. INTRODUCTION
TV-viewing is still a social experience but nowadays the audience
is connected through the Internet and makes use of social
networks like Twitter and Facebook to share thoughts in real-time
while watching TV content. The living room TV entertainment
environment has been enhanced by new interactive services that
keep the user engaged and active. According to Haythornthwaite
[6] media popularity is linked to social interactions in the new
media. They concluded that social ties and social media are
important to users’ media viewing habits. More recently, Harboe
et al [5], conducted an experiment examining the influence of
Social-TV watching, i.e., users watch television alone but get
instant notifications of what their friends and family are watching.
Oehlberg et al. [8] went one step further and proposed a series of
guidelines for designing distributed Social-TV viewing. In a
related user study Weisz et al. [9], examined the activity of
chatting while watching video online. Their technological solution
was designed to observe human factors and not improve usability.
In contrast, Cattelan et al. [3] implemented a system for chatting
and drawing over video. Zaletelj et al. [10] adapted the live shows
on-the-fly according to users’ explicit votes/audiences collected
from set-top-boxes. Several authors have proposed to analyse user
messages in different domains. Bollen [2] and Dan et al. [4]
addressed the domain of Twitter microposts and proposed to
detect the sentiment expressed in user messages. Recently,
Ariyasu et al. [1] proposed to detect the topics of twitter messages
and to associate them to the correct show. In our system, the show
is known and we proposed to infer the sentiment of the message
towards the show. Thus, we set out to develop a Social-TV system
prototype that allows users to chat with each other, in an
integrated way. The paper’s contribution is twofold:
Multi-screen interaction paradigm: the system User
Interface spans several screens and users interact with media
through two screens. The first screen is intended for media
visualization and it is assumed to be shared. The second
screen is intended for personal use – it is where the user can
watch previews, control the system and chat with peers.
Measuring TV chat feedback: the chat component is
powered by Google Channel API, thus a large base of users
already exist. Sentiment analysis technology observes the
sentiment expressed in opinions towards an item [7]. A novel
viewer feedback metering method is proposed by integrating
sentiment analysis technology into a Social-TV chat system.
With the proposed system, user messages are processed in real-
time by a sentiment analysis algorithm, allowing the plot of the
emotions felt by viewers, Figure 1. Another advantage of the
proposed visualization method is that viewers can get a quick
glimpse of the show’s popularity in the last few minutes.
Figure 1. The SentiTVchat player with the sentiment graph.
In the following section, we shall describe the system architecture
and components, and detail the chat analysis algorithm. An
experimental evaluation and the corresponding discussion
conclude this paper.
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. To copy
otherwise, or republish, to post on servers or to redistribute to lists,
requires prior specific permission and/or a fee.
EuroITV’12, July 4–6, 2012, Berlin, Germany.
Copyright 2012 ACM 978-1-4503-1107-6/12/07...$10.00.
161
Figure 2. Media player with SentiTVchat
graph.
Figure 3. Tablet device in
chat mode.
Figure 4. The personal control user
interface.
2. A SYSTEM FOR SOCIAL-TV CHAT
The SentiTVChat system, allows “Social-TV-viewers” to
communicate with each other during TV-viewing activities using
a simple chat interface, shown in Figure 3 and Figure 4. The main
goal was to develop a system for multi-screen TV-viewing
activities and to enable the system to collect chat interactions with
real-time analysis of the chat text to sense user moods.
2.1 System architecture
The system is divided in two parts: a Social-TV service and the
SentiTVchat service, see Figure 5. The Social-TV service is
responsible for the main media application and for binding user
devices in the same session with a common communication
channel. The chat system implements the chat service and
messages analysis technology.
Figure 5. System architecture.
2.2 Social-TV service
Users can access the system by using any Web browser, capable
of running JavaScript, which includes most modern browsers for
desktops, laptops and the browsers included in mobile platforms
such as iOS and Android. In Figure 2, we show the remote-control
client interface running on an Android tablet, for example. The
users can use mobile devices, such as tablets, to remote control the
TV and participate in chat rooms with other people.
TV-Screen: The Media Player UI
To build the TV-screen interface, in Figure 2, we used the
resources provided in the Google TV documentation for HTML5.
In addition, popular browsers started shipping preliminary support
for the Full screen API allowing the player to turn most modern
browsers into a full-blown media consumption screen.
Tablet-screen: The Personal Control UI
The remote-control functionality includes standard controls such
as Play/Pause and other control tasks, it also offers directional
keys for navigating between user-interface buttons on the TV-
screen. After authentication, a bidirectional channel is opened
between the Tablet-screen and the TV-screen. This allows the TV-
screen application to send updated media metadata information
such as program title and progress to the secondary devices.
To authenticate the user, we use two factors: the user’s Google
account login information and a random alphanumeric 5-digit
code that is available to the user in the TV-screen display corner.
This provides better security and allows a single user account to
be able to control a number of TV-screen devices.
Binding devices: a session based communication channel
To bind the user’s devices and to implement communication
channels between them we looked into Web Sockets. Current
browser support for Web Sockets is limited and implementations
differ substantially between browsers providing this API. Thus,
we decided to make use of Google App Engine APIs to build our
system so we could utilize the Channel API available on this
platform. The Channel API is similar to Web Sockets, it allows
Web applications to establish bidirectional communication
channels. However, browser support is improved, since the API is
provided to the client-side in a JavaScript library. This means that
any JavaScript capable mobile browser should be able to
communicate with a multitude of HTML5 ready devices running
the player, such as a Web TV, a PC or a tablet.
2.3 SentiTVchat service
The implemented chat system supports the chat communications
among users seamlessly. The Tablet-side UI gets the current TV-
channel from the updates received from the TV-screen
application. Thus, when users access the chat section in the
Tablet-screen it will automatically enter the corresponding chat
room for the TV-channel being watched on the TV-screen. We
considered that each TV-channel has a chat room with an identical
name, so, if the user is watching the tv1 channel, for example, the
Tablet-screen retrieves the URL: chat-host.tld/?room=tv1. The
page obtained displays the latest 1000 messages from the chat
room. Moreover, a subscription is registered on the server, so that
the client can receive further messages instantly. A subscription is
registered with the chat room, the user, and the communication
channel. When a message is received, it is relayed to all the
subscribers of the corresponding chat room through each of their
channels. However, to send messages, the client makes a POST
request to the /newMessage endpoint instead of using the channel.
162
The system also allows read-only subscriptions to chat rooms,
which do not require authentication. These were used to
implement SentiTVMeter. This feature visualizes the chat room
activity in a novel way: it draws a sliding line chart showing each
message “sentiment analysis” value and a weighted moving
average of the last 20 messages. This visualization was
superimposed into the TV-screen display, so users can always
monitor what is the general sentiment at each moment. The chart
is drawn client-side using the Canvas element and JavaScript,
making it lightweight, because the server just needs to annotate
the messages with the sentiment analysis value, and pipe it
through the communication channel.
2.4 Chat sentiment analysis
Sentiment analysis aims at detecting the preferences expressed
within user-comments. To tackle this problem in an automated
way, comments are represented as a vector of unigrams (words)
which occur within each user-comment. In sentiment analysis not
every word is useful for detecting the preferences of users [7],
thus, in our approach only adjectives, adverbs, verbs and nouns
are considered. In the following sections, the methodology to
represent and classify user-comments will be detailed formally.
2.4.1 Problem formulation
Consider a set of N user-comments, 11
{( , ),...,( , )}
NN
Dcp cp=,
where a user-comment ci is labeled as positive 1
j
p= or
negative 0
j
p=. Taking a machine learning approach and
considering the training set D, sentiment chat analysis aims at
learning a classifier function,
:[0,1]
ii
cpFÎ , (1)
such that for all new user-comment, i
cDÏ, the function F will
infer a polarity value pi = 1 for positive comments and pi = 0 for
negative comments. User-comments, are represented as a vector
of opinion words, i.e., ,1 ,
( ,..., )
ii im
cow ow= where each
component ,im
ow depicts the opinion word m of the user-
comment i. An opinion word is a word that can express a
preference, which can have different intensities and different
orientations (positive or negative).
2.4.2 Orientation and intensity of an OW
The orientation of an OW indicates whether a word expresses a
positive or negative preference. Turney [8] proposed the pointwise
mutual information (PMI) to estimate the orientation of a phrase.
This metric measures the degree of statistical dependence between
two words by observing the probability of co-occurring together
and individually. Hence, in this context the correlation will be
measured by using a positive, and negative, reference. Thereby
one can compute the semantic orientation (SO) of a word as
,
,2
,
(," ")(" ")
()log (," ")(" ")
ij
ij
ij
hits ow excellent hits poor
SO ow hits ow poor hits excellent
æö
÷
ç÷
ç
=÷
ç÷
ç÷
èø
, (2)
where ,
()
ij
hits ow , and for instance ,
(," ")
ij
hits ow excellent , are
given by the number of hits a Web search engine returns using
these keywords. In this paper, we tested the reference words listed
on Table 1.
Table 1. Semantic orientation and word polarity references.
Technique Word polarity references
T: Turney [8] “excellent”/”poor”
G: Generic “good”/”bad”
DS: Domain Specific “best movie”/”worst movie”
DS+T “excellent movie”/”poor movie”
Another factor to be considered is the intensity of an opinion
word. OWs may express different intensity values, such as:
contented” versus “ecstatic” [7]. Thus, the lexical resource
SentiWordNet [7], defining the intensity of opinion words, is
crucial in this phase. In this lexical resource, each feature is
associated with two numerical scores (positivity and negativity).
In this context, given the SO of an OW, SentiWordNet will return
its sentiment intensity. Therefore, the SentiWordNet (swn) for an
OW will be given by,
(), () 0,
() (), () 0
posSWN ow SO ow
swn ow negSWN ow SO ow
ì
ï>
ï
=í
ï£
ï
î
(3)
where ()posSWN ow corresponds to the positive score value
given by SentiWordNet and ()negSWN ow will correspond to the
negative score. In Table 2 the family column refers to the words’
family: N (noun), V (verb) and R (adverb). Regarding the
sentiment intensity, the lexical resource SentiWordNet associates
each word with a positive (posSWN) and negative (negSWN)
value.
Table 2. SentiWordNet weights for the sentence “Love it or
hate it, however someone tell me what on earth…”.
word family posSWN negSWN
love N 1.375 0.0
hate V 0.0 -0.75
however R 0.5 0.5
someone N 0.0 0.0
tell V 0.875 -0.625
earth N 0.0 -0.625
2.4.3 Classifier function
For classifying reviews, we used a linear classifier to assign a
confidence value to that review. The classifier identifies the
orientation and intensity of all opinion words of a comment
(
)
,1 ,
,...,
iiim
cowow= and computes its rating based on the
sigmoid function,
()
(
)
,
1
1exp
i
ij j
j
cb
ow w
F= +
+å. (4)
The weights
j
w are learned with a gradient descent algorithm to
train the function to rate comments between positive and negative.
The constant b is a bias factor for adjusting the sentiment curve.
2.4.4 Sentiment graph
Since the sentiment graph is plotted on the player screen (which
might be shared), all chat posts are plotted on the graph
anonymously. Figure 1 and Figure 2 shows the sentiment graph on
the player screen: instant messages are plotted in grey and a
163
smoothed curve is plotted in a bright green to offer a better sense
of the show popularity.
3. EXPERIMENTS
The system’s sentiment analysis module was evaluated on two
settings: a first experiment assessed the accuracy of the sentiment
analysis algorithm on labeled data; and a second experiment
validated the system on live chat data captured from Justin.tv.
3.1 Accuracy assessment
The language analyzer Freeling (http://nlp.lsi.upc.edu/freeling)
was applied to identify word families (nouns, verbs, etc). Later,
user-comments were split into sentence level with the Natural
Language Toolkit (http://www.nltk.org/home). A dataset from
IMDB reviews was used to assess the accuracy of the sentiment
analysis algorithm. The data is evenly split in two categories:
positive and negative. We have used 1400 training and 600 test
reviews along with a number of positive and negative reviews
equally divided.
Figure 6: F1-score for IMDB.
Figure 6 shows the classifier performance in terms of F-score, i.e.,
(
)
-score 2 F Precision Recall Precision Recall=+
, also
known as the harmonic mean. This figure illustrates the sentiment
analysis performance under different settings. We can observe
here the importance of choosing adequate reference words for the
Semantic Orientation (Equation 2): the best performance was
achieved with “excellent-movie”/“poor-movie”. This implies that
a combination of a domain specific term (“movie”) with general
string positive and negative terms (“excellent”/”poor”) is the best
setting. These results illustrate the sentiment analysis ability to
evaluate the sentiment within user comments.
3.2 Real-data experiment
To validate the system on real chat data, we collected chat data
from two live Justin.tv channels: 4744 sentences for the IGN Pro
League channel and 4553 sentences for the this WEEK in TECH
channel. Figure 7 illustrates the real-time sentiment analysis graph
of this data. These graphs present the results obtained with an
analysis of the sentiment expressed in the last 100 sentences.
There are several fluctuations on the instant messages (gray line)
and the popularity trends of the show are clearly visible on the red
curve. In this experiment, we observed that the algorithm was not
capable of correctly analyzing some of the messages since viewers
used slang expressions not recognized by the system.
Figure 7. Real-time sentiment analysis graph.
4. DISCUSSION
This paper proposed a novel Social-TV chat system that measures
in real time the viewers’ mood / show popularity. By measuring
and plotting viewers’ mood, users that are zapping through
channels will have instant access to the show popularity over the
last minutes. Additionally, this measure will be closer to real
viewers’ preferences than by simply measuring audiences.
Evaluation on the IMDB dataset demonstrated the ability of the
sentiment classifier in identifying the preferences within user-
comments. As future work, we will improve the sentiment
analysis for other languages and for chat slang.
Acknowledgements. This work has been funded by the
Foundation for Science and Technology by projects UTA-
Est/MAI/0010/2009 and PEst-OE/EEI/UI0527/2011, Centro de
Informática e Tecnologias da Informação (CITI/ FCT/ UNL) -
2011-2012.
5. REFERENCES
[1] Ariyasu, K. et al. 2011. Message analysis algorithms and their
application to social tv. EuroITV ’11, Lisbon, Portugal, Jun. 2011.
[2] Bollen, J. 2010. Determining the public mood state by analysis of
microblogging posts. Alife XII Conf. MIT Press. (2010).
[3] Cattelan, R.G. et al. 2008. Watch-and-comment as a paradigm
toward ubiquitous interactive video editing. ACM Transactions on
Multimedia Computing, Communications, and Applications. (2008).
[4] Dan, O. et al. 2011. Filtering microblogging messages for social tv.
Proceedings of the 20th international conference companion on
World wide web - WWW ’11 (New York, USA, Mar. 2011), 197.
[5] Harboe, G. et al. 2008. Ambient social tv: Drawing people into a
shared experience. ACM SIGCHI Human Factors in Computing
Systems. ACM.
[6] Haythornthwaite, C. 2001. The Strength and the Impact of New
Media. Proceedings of the 34th Annual Hawaii International
Conference on System Sciences ( HICSS-34).
[7] Liu, B. 2010. Sentiment analysis and subjectivity. Handbook of
Natural Language Processing. (2010), 978-1420085921.
[8] Oehlberg, L. et al. 2006. Social TV: Designing for distributed,
sociable television viewing. Proc. EuroITV (2006), 25–26.
[9] Weisz, J.D. et al. 2007. Watching together: Integrating text chat
with video. ACM SIGCHI Human Factors in Computing Systems..
[10] Zaletelj, J. et al. 2009. Real-time viewer feedback in the iTV
production. EuroITV 2009.
0.74 0.74 0.74
0.78
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
T:Turne y G:Ge neric DS: Domain
Specific
DS+T:Domain
Specific+Turne y
Fscore
164
... Complementariamente, dado que las definiciones ofrecidas aluden a la existencia de dos pantallas: una destinada al visionado del contenido audiovisual (el televisor), y otra adicional dedicada a la participación online, el objeto de análisis se vincula al fenómeno de la segunda pantalla o second screen (Hess et al., 2011;Mantzari et al 2008;Martins et al, 2012;Wilson, 2015;Giglietto & Selva, 2014;De Meulenaere, Bleumers & Broeck, 2015;Chorianopoulos & Lekakos, 2008;Gross et al. 2008;Harboe 2009;Lochrie & Coulton, 2012). ...
Article
Desde la invención de la televisión, el acto de visionado siempre ha tenido vinculado un componente social, pasando a convertirse en un agente socializador al que se le asocia una actividad de condición colectiva. Este aspecto se ha visto acrecentado de forma marcada en los últimos años desde la aparición y popularización de los smartphones y las redes sociales. Estos nuevos dispositivos han conseguido extender las posibilidades sociales vinculadas a la televisión, llevando, entre otras cuestiones, a la transformación de los modos de consumo o las formas de participación y rol de la audiencia, configurando lo que se entiende como televisión social. Focalizando el interés en el fenómeno, dada su relevancia, y teniendo en cuenta la importancia de la participación de la audiencia en el género info-show, el presente estudio analiza y describe los modos de apelación a la audiencia social empleados por productores y programadores en los programas de info-show más exitosos de Francia, España, Italia, Reino Unido y Alemania. El trabajo se centra en una observación comparativa de las prácticas, patrones y modos de actuación llevados a cabo por los programadores televisivos para potenciar la acción participativa. El análisis de contenido y herramientas específicas para el estudio de las plataformas online nos permiten aproximarnos a estas apelaciones a las audiencias.
... TV has been made more accessible from several types of devices. For example, a mobile phone enables people to engage in text or voice chat with friends while watching TV [3]. TV viewing remains a social experience, but current audiences are connected through the Internet and use social networks to share thoughts in realtime while watching TV content. ...
Conference Paper
Full-text available
Along with the development of Web 2.0, Internet users are gradually evolving from passive recipients of content to content creators. Considering recent technological advances and improved service designs, television (TV) content and its interactive methods have drastically changed. Moreover, various information devices ranging from wide-screen TVs in the living room to individual laptops or mobile phones have spread throughout the TV-watching environment. However, the increasing number of TV channels has given users a wider range of TV programs to choose from. Based on these issues, this paper proposes a social TV Electronic Programming Guide (EPG) interaction design under scenarios wherein users interact with a multi-screen TV, which aims at offering strengthened EPG services such as EPG navigation, EPG recommendation, and EPG searching, based on users' virtual social relationships.
Conference Paper
Availability and increased hardware performance of handheld devices with high resolution screens encourage users to exploit them as the second screen while watching television. Second screen is a term that refers to an additional screen of a handheld device such as a tablet or a smart phone, with an operating system capable of running desired installed applications. Desire to make additional DTV information available to the user which can be presented on the second screen tends to push the limit of digital television in that direction. This paper presents a framework to distribute and present both premium and free-to-air television content. The framework is based on server/client architecture and includes: TV remote control, streaming of television services, additional content (Electronic Program Guide, reminders...), data protection of streamed services to client devices. The system architecture consists of a media server and its clients. Media server is a Set-Top box with Android OS. Main duty of media server is to receive and process DTV content received from broadcasters and distribute the content to clients (second screen devices). Client application is developed on various OS/Platforms: Android and iOS. Multimedia content can be streamed using supported protocols: HTTP, RTP/RTSP, and adaptive streaming protocols (HLS and MPEG-DASH). Streamed content is protected using DTCP-IP. The main objective of the paper is to describe the utilization of before mentioned standards in a framework to use the DTV data and functions beyond the scope of the main TV screen. The client application also implements the remote controller GUI in order to use one device to control the TV.
Article
Full-text available
This paper presents the concept of viewer feedback in the production of multi-channel, live TV shows. The IST-FP6 project "LIVE - Live Staging of Media Events" is developing a production support system which will provide personalized content recommendations as well as an overview about viewers' feedback for the production team in real-time. The paper outlines a concept of a feedback system consisting of ipTV application for the consumer, the feedback collection and analysis components, and the voting control and feedback presentation modules in the TV production. The LIVE Feedback system enables the production team to observe preferences of the TV viewers in real-time, during the live production of the show, and on the other hand gives viewers the possibility to actively influence the creation of live TV content through voting.
Article
Full-text available
The literature reports research efforts allowing the editing of interactive TV multimedia documents by end-users. In this article we propose complementary contributions relative to end-user generated interactive video, video tagging, and collaboration. In earlier work we proposed the watch-and-comment (WaC) paradigm as the seamless capture of an individual's comments so that corresponding annotated interactive videos be automatically generated. As a proof of concept, we implemented a prototype application, the WaCTool, that supports the capture of digital ink and voice comments over individual frames and segments of the video, producing a declarative document that specifies both: different media stream structure and synchronization. In this article, we extend the WaC paradigm in two ways. First, user-video interactions are associated with edit commands and digital ink operations. Second, focusing on collaboration and distribution issues, we employ annotations as simple containers for context information by using them as tags in order to organize, store and distribute information in a P2P-based multimedia capture platform. We highlight the design principles of the watch-and-comment paradigm, and demonstrate related results including the current version of the WaCTool and its architecture. We also illustrate how an interactive video produced by the WaCTool can be rendered in an interactive video environment, the Ginga-NCL player, and include results from a preliminary evaluation.
Article
Full-text available
Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events and their properties. Opinions are usually subjective expressions that describe people's sentiments, appraisals or feelings toward entities, events and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people's positive or negative sentiments. Much of the existing research on textual information processing has been focused on mining and retrieval of factual information, e.g., information retrieval, Web search, text classification, text clustering and many other text mining and natural language processing tasks. Little work had been done on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others' opinions. This is not only true for individuals but also true for organizations. One of the main reasons for the lack of study on opinions is the fact that there was little opinionated text available before the World Wide Web. Before the Web, when an individual needed to make a decision, he/she typically asked for opinions from friends and families. When an organization wanted to find the opinions or sentiments of the general public about its products and services, it conducted opinion polls, surveys, and focus groups. However, with the Web, especially with the explosive growth of the user-generated content on the Web in the past few years, the world has been transformed. The Web has dramatically changed the way that people express their views and opinions. They can now post reviews of products at merchant sites and express their views on almost anything in Internet forums, discussion groups, and blogs, which are collectively called the user-generated content. This online word-of-mouth behavior represents new and measurable sources of information with many practical applications. Now if one wants to buy a product, he/she is no longer limited to asking his/her friends and families because there are many product reviews on the Web which give opinions of existing users of the product. For a company, it may no longer be necessary to conduct surveys, organize focus groups or employ external consultants in order to find consumer opinions about its products and those of its competitors because the user-generated content on the Web can already give them such information.
Conference Paper
Full-text available
Watching video online is becoming increasingly popular, and new video streaming technologies have the potential to transform video watching from a passive, isolating experience into an active, socially engaging experience. However, the viability of an active social experience is unclear: both chatting and watching video require attention, and may interfere with one another and detract from the experience. In this paper, we empirically examine the activity of chatting while watching video online. We examine how groups of friends and strangers interact, and find that chat has a positive influence on social relationships, and people chat despite being distracted. We discuss the benefits and opportunities provided by mixing chat and video, uncover some of the attentional and social challenges inherent in this combination of media, and provide guidance for structuring the viewing experience.
Conference Paper
Full-text available
ABSTRACT Weexamine,how ,ambient ,displays can augment ,social television. Social TV 2 is an interactive television solution that incorporates ,two ,ambient ,displays to convey ,to participants an aggregate viewof their friends’ current TV- watching status. Social TV 2 also allows users to see which television shows,friends and family are watching and send lightweight,messages ,from ,within ,the ,TV-viewing experience. Through a two-week ,field study we found ,the ambient displays to be ,an integral part of the experience. Wepresent,the results of our ,field study with a discussion of the implications for futuresocial systems in the home. Author Keywords Social television, interactive television, social presence awareness, ambient displays, field trial ACM Classification Keywords
Conference Paper
Full-text available
Social TV was named one of the ten most important emerging technologies in 2010 by the MIT Technology Review. Manufacturers of set-top boxes and televisions have recently started to integrate access to social networks into their products. Some of these systems allow users to read microblogging messages related to the TV program they are currently watching. However, such systems suffer from low precision and recall when they use the title of the show as keywords when retrieving messages, without any additional filtering. We propose a bootstrapping approach to collecting microblogging messages related to a given TV program. We start with a small set of annotated data, in which, for a given show and a candidate message, we annotate the pair to be relevant or irrelevant. From this annotated data set, we train an initial classifier. The features are designed to capture the association between the TV program and the message. Using our initial classifier and a large dataset of unlabeled messages we derive broader features for a second classifier to further improve precision.
Article
Full-text available
Media research has shown that people enjoy watching television as a part of socializing in groups. However, many constraints in daily life limit the opportunities for doing so. The Social TV project builds on the increasing integration of television and computer technology to support sociable, computer-mediated group viewing experiences. In this article, we describe the initial results from a series of studies illustrating how people interact in front of a television set. Based on these results, we propose guidelines as well as specific features to inform the design of future “social television” prototypes.
Chapter
Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events, and their properties. Opinions are usually subjective expressions that describe people’s sentiments, appraisals, or feelings toward entities, events, and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people’s positive or negative sentiments. Much of the existing research on textual information processing has been focused on themining and retrieval of factual information, e.g., information retrieval (IR), Web search, text classification, text clustering, and many other text mining and natural language processing tasks. Littleworkhadbeendone on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others’ opinions. This is not only true for individuals but also true for organizations.
Article
This paper proposes algorithms for analyzing Twitter messages. These algorithms play an important role in our 'Intelligence Circulation System', which provides several services for social TV. Twitter users often post messages about on-air TV programmes. The Intelligence Circulation System analyses these messages by using programme-related information and generates several outputs based on the analysis results. Outputs are provided to both viewers and broadcasters. The algorithms, which were designed by taking into account the characteristics of Twitter messages related to TV programmes, use auxiliary programme information, the similarity between messages, and the time series of the messages. Evaluation of our algorithms using Twitter messages about the World Cup showed that topic extraction was about 70% accurate and that message classification reached a precision of 0.85 and a recall of 0.74. Five services have been implemented in the Intelligence Circulation System by adapting the algorithms to each service.
Article
Extended Abstract Microblogging is a form of online communication by which users broadcast brief text updates, also known as tweets, to the public or a selected circle of contacts. A variegated mosaic of microblogging uses has emerged since the launch of Twitter in 2006: daily chatter, conversation, information sharing, and news commentary, among others (Java et al, 2007). Regard-less of their content and intended use, tweets often convey pertinent information about their authors mood status. As such, tweets can be regarded as temporally-authentic microscopic instantiations of public mood state (O'Connor et al, 2010). Here we perform a sentiment analysis of all public tweets broadcasted by Twitter users between August 1 and December 20, 2008. For every day in the timeline, we extract six dimensions of mood (tension, depression, anger, vigor, fatigue, confusion) using an extended version (Pepe and Bollen, 2008) of the Profile of Mood States (POMS), a well-established psychometric instrument (Norcross et al, 2006; McNair et al, 2003). We compare our results to fluctuations recorded by stock market and crude oil price indices and major events in media and popular culture, such as the U.S. Presidential Election of November 4, 2008 and Thanksgiving Day (see Fig. 1). We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. In addition, we found long-term changes in public mood that may reflect the cumulative effect of various underlying socio-economic indicators. With the present investigation (Bollen et al, 2010), we bring about the following methodological contributions: we argue that sentiment analysis of minute text corpora (such as tweets) is efficiently obained via a syntac-tic, term-based approach that requires no training or machine learning. Moreover, we stress the importance of measuring mood and emotion using well-established instruments rooted in decades of empirical psychometric research. Finally, we speculate that collective emotive trends can be modeled and predicted using large-scale analyses of user-generated content but results should be discussed in terms of the social, economic, and cultural spheres in which the users are embedded.