Twitter is a platform which may contain opinions, thoughts, facts and other information. Within it, many and various communities are originated by users with common interests, or with similar ways to feel part of the community. This paper presents a possible combined approach between Social Network Analysis and Sentiment Analysis. In particular, we have tried to associate a sentiment to the nodes of the graphs showing the social connections, and this may highlight the potential correlations. The idea behind it is that, on the one hand, the network topology can contextualize and then, in part, unmask some incorrect results of the Sentiment Analysis; on the other hand, the polarity of the feeling on the network can highlight the role of semantic connections in the hierarchy of the communities that are present in the network. In this work, we illustrate the approach to the issue, together with the system architecture and, then, we discuss our first results.
An increasing number of people is progressively approaching to the social networking sites, which become more and more popular and complex: within their context many and various communities are originated by users with common interests or with similar ways to feel part of the community. The kinds of analysis as well as information that can be extracted from the social networking sites are varied and increasingly appealing both to the world of marketing and to the social or political one. The classical approach to Social Network Analysis allows to study the topology of a network through the connections that develop within it, giving rise to a hierarchy of communities within the main topic. Furthermore, certain types of social networks, like Twitter, allow to track relationships also in those cases in which knowledge is not mutual: simply a node is a follower of another node. The number of followers defines in part the popularity of a node within the network, but it is not able to point out if this popularity is positive or negative.
On the other hand, the explosion of data on the Web has made the research in automatic cataloging of texts increasingly interesting, as well as the extraction of information or meta-information and the Sentiment Analysis of a review, an emotion, a tweet. Moreover, in this area the explosion of microblogging, and the use of a simple “like” or a retweet as a form of acceptance or sounding board for information as well as the dynamism and the speed with which everyone reads and writes content make the analysis of these opinions hard, if you use the methods of text mining, while they introduce, or amplify, new issues and problems for sentiment analysis (such as citations, irony, role of emoticons) that are difficult to deal with regardless the context in which they are written.
This paper presents a combined approach between Social Network and Sentiment Analysis. In particular we have tried to introduce some kind of information about sentiments on the graphs showing the results of the Social Network Analysis (SNA): in this way we hope to highlight other potential correlations among the nodes of net under examination. The idea behind it is that, on the one hand, the network topology and the selected topics of the network can contextualize and then, in part, unmask some incorrect results of the Sentiment Analysis (SA), and, the other hand, the polarity of the feeling on the network can highlight the role of semantic connections, as a possible foundation for the organization and the hierarchy of the communities highlighted by the Social Network Analysis.
In the following, after a brief description of the background, the system architecture will be showed, together with the choices which we made, then some results obtained from the initial evaluation of the system will be discussed. 2
SNSs are a collection of web-based services that allow users to build a profile within
the system and define a list of other users with whom they have some kind of
connection [
In this research work, we built a system for social network and sentiment analysis,
which can operate on Twitter data, one of the most popular social networks. The
analysis of large amount of data is an exciting challenge for researchers, but it is also
crucial for all those who work at different levels in the current information society:
Twitter has been the subject of attention from researchers as early as 2009 [
Some recent studies about American candidates are important for understanding
how public sentiment is shaped and its polarization [
Another quite important problem to work on Twitter data is how to automatically
collect a corpus for SA and, in general, Opinion Mining purposes: example of how to
perform this task is in, for example, [
In this paragraph we describe our system for social network and sentiment analysis, which can operate on Twitter data.
Twitter is a platform which may contain opinions, thoughts, facts, references to images and other media and, recently, stream video filmed live and put online by users. So it is more than just a SNCs in which a user displays and increases their social relationships, it is a real communication channel in which a user can choose its topics and its node of reference according to his interests and culture.
A study of the network topology and the number of interconnections of a node are able to highlight the communities in the network and also in part to how the information is propagated, but they are not able to say anything about the degree of agreement and cohesion of members of a community. To solve this task you need to carry out an investigation into the semantic content of the messages.
Compared to the problems of classic data mining, sentiment analysis shows many difficulties in terms of effectiveness. This is mainly due to the subtle distinction that exists between positive and negative sentiment or between neutral and positive one. Let us suppose for example a sentence containing irony or sarcasm, where the interpretation of the meaning is strictly subjective. In this case, two human beings may be in disagreement about the real feeling that it expresses. Furthermore, not always the opinions are expressed through the use of opinion words, in many cases the special language constructs (such as the figures of speech) come into play.
Difficulties also are due to the use of non-formal expressions and slangs that do not belong to the vocabulary of a language. These terms are often used in an intensive way to express a particular opinion or a certain mood.
Additional problems are due to the domain of the subject: in particular we note that the feelings that are expressed by a word are often dependent on the topic. We look at this sentence as an example: “It's quiet!”. It shall render a positive opinion if we are talking of a car engine, but it reveals a disapproval if the matter of discussion is a phone.
As a microblogging service, Twitter is used to publish short messages counting a maximum of 140 characters (tweets). This characteristic if one side it may seem easier because it forces people to take a position, on the other side the few words not allow the user to repeat concepts or emotions: he rather uses slangs shared by the community, emoticons and punctuation.
Besides the ease of retweet increases the difficulty in perceiving what is the real feeling of the user who runs it and the intense use of citations can also distort the sentiment enclosed in the tweet.
However, by combining the information of SA with those of the SNA we can hope to disambiguate some actual cases and the opportunity to know the slang of the channel under examination can improve the efficiency of machine learning algorithms for the SA. 3.1
As a social networking platform, Twitter is structured as a directed graph, in which each user can choose to follow a number of other users (followees), and can be similarly followed by other users (followers). Thus, the “follow” relationship is asymmetrical, it does not require mandatory acknowledgement, and it is essentially used to receive all public messages published by any followee user.
Consequently, in our analysis we collected three types of data (Fig. 1): the User type represents users' profiles; the Tweet type represents posted messages; the Friend type represents the “follow” relationships among users.
Apart from data obtained directly from Twitter, we added a field to both tweets and users, to associate a sentiment with them, according to the result of our SA. Currently if a user posts more than one tweet on the net, we decided to associate to him the sentiment of the last tweet that he posted. 3.2
As a communication medium, tweets have a quite peculiar nature. Some distinguishing features of communication on Twitter are related to technical aspects; those include length of text, tags, urls, etc. Other features may be classified as idiomatic use of the medium, and create a sort of Twitter culture.
As a start, a tweet may contain many elements that are not significant for our classification, and can thus be dropped though a filtering process. To polish the message, we defined various filters, that can applied in a customizable sequence. An example is shown in Fig.2.
A first filter eliminates useless tokens such as: the “RT” sequence; the @ character and the whole following user name; the # symbol, but not the following topic name, which is kept in the message. The topic name is also removed, though, when it coincides with the name of the channel where tweets are collected from.
A second filter applies the language specific rules. It includes an orthographic correction of the message, which is used to remove unknown words (in the example: “icantbelievit”) and other filtering processes for stemming and removal of stopwords.
Finally, another filter separates all punctuation symbols from the text, and organizes them as single-character words. Even if smiles sequences, repeated question and exclamation marks are kept as aggregates because they are important patterns for the classification.
The final result of the filtering process is a word vector, which is then submitted to a set of classifiers.
We use a set of classifier to identifying the following classes of messages:
undiscriminated, objective, subjective, positive, negative. Moreover, there is a class in
which the system put all the tweets that are too short to be classified. The system is
organized as a simple hierarchy of agents, mimicking the hierarchy of sentiment
classes. In fact, since objective messages have no polarity by definition, the classifier
for positive and negative sentiments is only applied to subjective messages (see Fig.
3). One advantage of this framework for classifiers is the ease with which you can add
classifiers trained to identify other emotions. In fact, hierarchical classification has
been applied successfully in a number of studies, for information retrieval [
Each classifier is based on Multinomial Naive Bayes algorithm, that one of the most popular methods used in SA. We have selected it because it seems to be the most suitable to generate and process large sets of features. In fact, instead of generating a training set by hand, we aimed at realizing an automated (or at least semiautomated) process for obtaining good training sets. In our methodology, the training sets are obtained through the automatic elaboration of some particular streams of tweets and comments, obtained directly from Twitter, without any manual classification. Thus, each training set may contain an important number of wrong data. Nevertheless, we show that they can be used to obtain useful results.
About the objectivity/subjectivity classifier, we adopted a similar strategy to [
About the polarity classifier, we used different sources, thus generating training
sets which do not overlap with those about objectivity/subjectivity. In fact, we used
sources of mostly positive or negative messages, respectively. On the one hand, those
sources should fit the particular setting of Twitter (short messages, idiomatic
expressions, smiles, etc.). On the other hand, they should not be specific to a
particular topic or context (sport, music, etc.). Thus, we dropped the idea of collecting
messages about particular events, mostly generating either positive or negative
sentiments. Instead, we collected messages, using generic yet polar terms as queried
hashtags. In particular, we used the following channels to gather positive content:
#adorable, #awesome, #beautiful, #beauty, #cool, #excellent, #great. We used the
following channels to gather negative content: #angry, #awful, #bad, #corrupt,
#pathetic, #sadness, #shame. Actually, such terms have been chosen quite empirically,
taking into account the quality of training sets they generated. But they could be
selected from WordNet-Affect [
In this way, the training set is generated in an automated fashion, as a list of tweets. Each tweet is associated with its supposed class, in accordance to its source. In fact, the training set is not perfect, as it contains messages gathered from public channels. However, a training set of this kind can be generated easily and in a methodical way, from real and updated Twitter messages. Moreover it is possible to extend this approach to train a classifier to recognize feelings which are written in a particular slang.
In Fig. 4 there are some examples of features which are selected by the classifiers together their probabilities. It is worth noting that these are consistent with what we expected: the emoticons ‘:)’ has a high probability of being in positive phrases, while the pattern ‘!!!’ is very significant for the classifier of the subjectivity but it is a useless feature to determine the polarity of a tweet. 4
type of the published tweets and the instances used for training the classifiers. All data
were downloaded between 2015-02-02 and 2015-02-10. The awarding of the Grammy
took place on 2015-02-08. The social network (shown in Fig. 5) consists of a total of
5570 nodes (users) and 6886 arcs (“follows” relationships). Nodes are deployed
according to the ForceAtlas2 algorithm [
Looking at the figure, it is possible to notice that the network topology is consistent with the nature of the considered case. In fact, most of the channel consists of independent users (or small groups of users) that express their opinion about the artist; however, in the central part of the network there are some major communities.
As shown in Fig. 5, the prevailing sentiment detected from the classifier is the negative one. Performing an analysis on a sample of tweets in the network, we noticed that many sentences are actually quotes of songs. These messages contain melancholic and sad phrases, and are therefore classified as negative. Considering that a quote is generally an appreciation for the artist, most users classified as negative are actually positive users. This is a typical example of a classic problem of misunderstanding of the SA: the system, while classifying correctly the tweet, misses the assessment of the feeling because it can not evaluate the tweet together with its context.
For evaluating the performances of our system, we conducted a simple survey through a group of persons in our department. In this way, we selected and classified 100 messages that show a clear opinion on the singer. Then, we used those messages as a test. The results of the classifiers showed an accuracy of 84% for the polarity and 88% for subjectivity.
In the network periphery (at the top-right corner of Fig. 5), it is possible to notice a small group of users whose feeling is completely positive. After a careful analysis of users' tweets in this small group, it was found that these posts are mainly retweets and the original messages are only two. Of these two messages, the first is actually positive, while the other one is objective. This episode shows how some errors of assessment can have important impact on larger communities.
In addition to the #Samsmith channel, we considered the social network associated with the #Ukraine channel, trying to obtain some particularly significant results, above all from the point of view of network topology. In fact, the crisis in the region could lead to a quite sharp division on the Web. This work is still in progress, nevertheless we can show here some results which we already obtained.
At the moment we have downloaded the data, the network consisted of: • 26131 nodes • 1163588 edges
In Fig. 6, it is possible to see the main results of our analysis on the network. The more evident thing to notice, is that the prevailing color in the network is blue (objective tweets), and the next one is red (negative tweets). Given the nature of the channel we are considering, which essentially reflects a social tragedy, the sentiment we have found through the analysis is quite plausible. However, analyzing some random messages, we have noticed a number of errors in the classification of these tweets. In particular, some objective sentences are often classified as negative ones, while some sentences expressing essentially hope (and thus positive) are classified as objective ones. In our opinion, the reason for these errors is related to the type of features contained in the model of classifiers, which possibly are not a good fit for this particular case study.
The case of Ukraine has been discussed quite largely in traditional media, too, for
the supposed role of “trolls” operating on new media to influence the public opinion
[
We used the community detection algorithm provided with Gephi, at various
resolution levels [
Full Network 51.706 7 1 2.511 10591776 0.016 0.420 873460
53.021
Community 2 42.649 7 4 2.248 3152400 0.030 0.480 540526 6 4 2.334 2014980 0.030 0.414 281524 Looking at data reported in Table 1, it is easy to notice that the two communities, corresponding to opposing factions in the crisis, have a quite similar size. Moreover, also their main features are quite similar. This seems to indicate that the two camps have a quite similar internal social organization, at least at the macroscopic level. Nevertheless, both the communities have high density, almost doubling the value of the whole network. This means that, in fact, there is a quite clear separation between those two communities, which have relatively few shared connections.
Our sentiment analysis has not highlighted significant differences in the emerging opinions in the two communities. In fact, they largely share the same negative outlook of the whole network. This is an issue that we plan to analyse in deeper detail in future. The emerging sentiment in each camp may also vary during time, and in particular in correspondance with major events and turnpoints in the crisis.
This study reports the initial results we obtained from the synthesis of Social Network Analysis and Sentiment Analysis. We experimented our approach on a couple of Twitter channels, as case studies. In particular, we considered the #SamSmith channel during the Grammy Awards in 2015, and the #Ukraine channel during the crisis of 2014. Apart from the particular results, a methodology and some guidelines for the automatic classification of Twitter content have been discussed.
The implemented software allows: (i) to get a training set for the classifiers that deal with Sentiment Analysis, and (ii) to make a thorough study of the network topology. The study of the global sentiment within the network has highlighted the typical problems of Sentiment Analysis (irony, sarcasm, lack of information, etc.). Additionally, some peculiar problems of the considered channel were also detected (such as the quotes of songs). Also, the analysis of biased channels, may pose additional difficulties.
The performances obtained by the classifiers during tests conducted on the training set and the analysis of the case studies have shown good and promising results.