A content-based visualization system is presented for the sentiment analysis on social networks. The methodology implemented was focused on the semantic processing taking into account the content in the public user's opinions. In our approach the comments were handled as excerpts of knowledge. During the visualization the social graph is displayed presenting the polarity and sentiment status for each comment. Moreover a web mapping tool retrieves comments in a radius based on the location source(geographic) or concepts related to geographic entities and spatial relations in the comment(conceptual).
Nowadays the huge information transmitted on social networks has become a
rich source of information for the human understanding as well as a way of
expression where the users share their sentiment status and personal opinions
through comments. The sentiment identication can classify comments as
positive or negative(polarity) and unveil emotions such as anger, trust, sadness ,etc.,
on certain topics or users. Moreover the sentiments presented in the opinions can
be relevant in the design of custom services, social plans for public health,
marketing, e-commerce,etc. On the basis of these motivations, we developed a web
system for real-time monitoring of sentiment information in social networks for
specic targets(public events or users). The system is able to display the social
graph structure, sentiment information related as well as retrieve comments by
source’s location or words geo-referenced in the text by means of a web mapping.
The methodology implemented handles the comments as excerpt of the
knowledge, in this gap we prioritized the semantic level, sense and meaning of the whole
comment. The proposal computed semantic similarity measures, conceptual
expansion, graph theory algorithms and disambiguation using a multi domain
knowledge base. The methodology is composed for the following stages: the stage
of social networks discovery retrieves opinions from events or public proles by
reading their comments. Consequently the stage of knowledge processing
constructs the formal representation for each comment by using an knowledge base
and graph algorithms such as steiner tree [
This section describes the system that implemented our methodology. We present examples of the sentiment analysis and their visualization.
In order to explain the sentiment analysis an example was processed from Twitter in the CNN News account. The comment considered is: "a number of people feared dead after a dam bursts in kenya with hundreds left homeless ocials say". The table 1 presents the closest sentiment and a polarity value assigned by our methodology to each concept.
Finally the methodology estimates the total polarity and main sentiment presented in the comment. The values established were for polarity: -0.1875 and main sentiment: NRC_Anger.
Additionally some relevant results from Twitter account CNN News are presented. The table 2 denes the main sentiment and polarity value assigned by our methodology to the comments. We noticed a better and more trustworthy classication using the basic sentiments instead of polarity (average).
Title Suppressed Due to Excessive Length Sentiment trust trust anger anger sadness sadness joy
Polarity Comment 0.2916667 This couple found a buried safe containing $52,000 worth of money, gold and jewelry in their backyard, but didn’t keep it -0.15 In an eort to keep conversations and search results on topic, Twitter announced it will use new "behavioral signals" to push down more
tweets that "distort and detract" 0.04166667 A massive poaching ring in Oregon and Washington is accused of killing more than 200 animals including deer, bears, cougars, bobcats
and a squirrel 0.041666687 An estimated 239,000 girls under the age of ve die in India each year due to neglect linked to gender discrimination, a new study nds 0.25 @CNN Her father had a heart surgery and cant walk so -0.25 Teen develops ’wet lung’ after vaping for just 3 weeks 0.125 I am proud to be a woman and a feminist. The politics of Meghan
Markle
Table 2. Other examples processed in twitter
In the visualization the results are displayed in the system by means social graphs and web mapping. Regarding the social graph it describes the network’s structure and its sentiment information related to comments by colors. For instance the gure 1 presents the polarity and sentiment graphs for the CNN news account. Regarding the nodes the darkest blue represents the user target and light blue for users farther. Particularly in the polarity graph the nodes with gray color represents neutral comments and the scale between green and red for positive to negative polarities respectively. On the other hand in the sentiment graph each comment has a sentiment represented by a dierent color.
In addition the web mapping tool retrieves comments by location which can be geographic(location source) or conceptual(Geo-referenced concepts and relations), The gure 2 retrieves comments by conceptual processing using the keyword Arkansas in a distance of 1000 km. The comment that contains in its description the concept memphis is retrieved. In this poster a content-based methodology and its implementation were proposed for the sentiment identication. The novelty of the presented our approach is the capability of handling the comments as excerpts of knowledge. We provided a mechanism of semantic processing using knowledge graphs, graph theory algorithms, semantic similarities and disambiguation. Our implementation can be a relevant tool for studying the impact of events and users in the society. Moreover the sentiment analysis in social networks can contribute in the public health and design of custom services. 5
This work was supported by CONACYT and JSPS KAKENHI Grant Number JP17H01789.