Behaviour Analysis Based On Social Media
What is the problem?
“What other people think” has always been an important piece of information for most of us during the decision-making process. Long before awareness of the World Wide Web became widespread, many of us asked our friends to recommend an auto mechanic or to explain what they think of someone based on the behaviour he/she shows, who they were planning to vote for in local elections, requested reference letters regarding job applicants from colleagues, or consulted Consumer Reports to decide what dishwasher to buy. But the Internet and the Web have now (among other things) made it possible to find out about the opinions and experiences of those in the vast pool of people that are neither our personal acquaintances nor well-known professional critics that is, people we have never heard of. And conversely, more and more people are making their opinions available to strangers via the Internet.
Then again The Web contains a wealth of opinions about products, politicians, and more, which are expressed in newsgroup posts, review sites, and elsewhere. As a result, the problem of “opinion mining” has seen increasing attention over the last three years from and many others. This paper focuses on product reviews, though our methods apply to a broader range of opinions. Product reviews on Web sites such as amazon.com and elsewhere often associate meta-data with each review indicating how positive (or negative) it is using a 5-starscale, and also rank products by how they fare in the reviews at the site. However, the reader’s taste may differ from the reviewers’. For example, the reader may feel strongly about the quality of the gym in a hotel, whereas many reviewers may focus on other aspects of the hotel, such as the decor or the location. Thus, the reader is forced to wade through a large number of reviews looking for information about particular features of interest. But the question is how many of those opinions are correct. There are possibilities that some opinions are actually wrong but are showing positive towards some certain things. And if the opinion is not positive, so what are traits which will identify that the opinions are wrong or negative.
Another problem can be arisen is that opinions can be mixture of both positive and negative sides and the solution would be how to solve this ambiguity.
Why is it interesting
Others’ opinions can be crucial when it’s time to make a decision or choose among multiple options. When those choices involve valuable resources (for example, identifying people’s emotions or people’s perspective) people often rely on their past activities. Until recently, the main sources of information were friends and specialized magazine or websites. Now, the “social web” provides new tools to efficiently create and share ideas with everyone connected to the World Wide Web. Forums, blogs, social networks, and content-sharing services help people share useful information. This information is unstructured, however, and because it’s produced for human consumption, it’s not something that’s “machine process able.”Capturing public opinion about social events, political movements, company strategies, marketing campaigns, and product preferences is garnering increasing interest from the scientific community (for the excitingopen challenges), and from the business world (for the remarkable marketing fallouts and for possible financial market prediction).
The resulting emerging fields are opinion mining and sentiment analysis. Although commonly used interchangeably to denote the same field of study, opinion mining and sentiment analysis actually focus on polarity detection and emotion recognition, respectively. Because the identification of sentiment is often exploited for detecting polarity, however, the two fields are usually combined under the same umbrella or even used as synonyms. Both fields use data mining and natural language processing (NLP) techniques to discover, retrieve, and distil information and opinions from the World Wide Web’s vast textual information. Mining opinions and sentiments from natural language is challenging, because it requires a deep understanding of the explicit and implicit, regular and irregular, and syntactical and semantic language rules. Sentiment analysis researchers struggle with NLP’s unresolved problems: co reference resolution, negation handling, anaphora resolution, named-entity recognition, and word-sense disambiguation.
Opinion mining is a very restricted NLP problem. Narrative text is often especially prone to having emotional contents. In the literary genre of fairytales, emotions such as HAPPINESS and ANGER and related cognitive states, e.g. LOVE or HATE, become integral parts of the story plot, and thus are of particular importance. Moreover, the story teller reading the story interprets emotions in order to orally convey the story in a fashion which makes the story come alive and catches the listeners’ attention. In speech, speakers effectively express emotions by modifying prosody, including pitch, intensity, and durational cues in the speech signal. Thus, in order to make text-to-speech synthesis sound as natural and engaging as possible, it is important to convey the emotional stance in the text. However, this implies first having identified the appropriate emotional meaning of the corresponding text passage.
Why is it hard?
Online Social Networks (OSNs) offer a rich set of different communication and content sharing functionalities to their users and change the way how people interact. They support basic human needs such as communication, socializing with others and reputation building. Facebook is the dominating social networking service in the world that attracts more than one billion people. Because of their importance for both, the individual users and the modern society, OSNs are subject in many research areas. Inter alia, researchers examine user interactions, the information diffusion or novel social networking architectures.
An in-depth understanding of user behaviour in OSNs can provide major insights into human behaviour, and impacts design choices in social platforms and applications. However, user behaviour in OSNs is still not deeply understood, because researchers have only limited access to behavioural data. The main findings are that Facebook sessions are very short, compared with assumptions in the literature and users’ content contributions are extremely disparate in type and quantity. A major share of newsfeed stories is posted by a minority of users and consists of reshared, liked or commented issues rather than original user-generated content. Facebook manages to compensate this lack of high quality content by transforming commercial posts into regular newsfeed content. Also, actions that cause little effort, such as reshares and likes, recently became more popular than status updates and comments With this work, we contribute to better understand the usage of Facebook with respect to churn, content contribution and consumption, as well as communication patterns.
The challenge here is that users may well have pre-existing attitudes and opinions — both positive and negative —towards others with whom they share certain characteristics, and hence before arbitrarily making such suggestions to users, it is important to be able to estimate these attitudes from existing evidence in the network. For example, if A is known to dislike people that B likes, this may well provide evidence about A’s attitude toward B.
Why hasn’t been it solved before
A vast amount of related work in the field of user behaviour in OSNs has been done by now. Prior work can be classified by the research discipline (e.g. computer science and psychology), the data collection method as well as the social network that has been analyzed. There is a study about user behaviour on the most popular OSN, Facebook, with 2071 participants from 46 countries. We elaborate how Facebookers orchestrate the offered functions to achieve individual benefit in 2014 and evaluate user activity changes from 2009 till 2014 to understand the development of user behaviour. The design and development of Stanford CoreNLP, a Java (or at least JVM-based) annotation pipeline framework, which provides most of the common core natural language processing(NLP) steps, from tokenization through to co reference resolution. The motivations of this system were to be able to quickly and painlessly get linguistic annotations for a text, to hide variations across components behind a common API, to have a minimal conceptual footprint, so the system is easy to learn.
In this paper, using a large amount of data collected from Twitter, the blogosphere, social networks, and news sources, we perform preliminary research to investigate if human behaviour in the real world can be understood by analyzing social media data. The goals of this research are twofold: (1) determining the relative effectiveness of a social media lens in analyzing and predicting real-world collective behaviour, and (2) exploring the domains and situations under which social media can be a predictor for real-world's behaviour. We develop a four-step model: community selection, data collection, online behaviour analysis, and behaviour prediction. The results of this study show that in most cases social media is a good tool for estimating attitudes and further research is needed for predicting social behaviour.
