users across various platforms. Social media have therefore become a primary source of information for business intelligence.
Figure 1.3: Number of LinkedIn members from the first quarter of 2009 to the first quarter of 2014 (in millions) provided by Statista.
There are several means of interaction in social media platforms. One of the most important is via text posts. The natural language processing (NLP) of traditional media such as written news and articles has been a popular research topic over the past 25 years. NLP typically enables computers to derive meaning from natural language input using the knowledge from computer science, artificial intelligence, and linguistics.
NLP for social media text is a new research area, and it requires adapting the traditional NLP methods to these kinds of texts or developing new methods suitable for information extraction and other tasks in the context of social media.
There are many reasons why the “traditional” NLP are not good enough for social media texts, such as their informal nature, the new type of language, abbreviations, etc. Section 1.3 will discuss these aspects in more detail.
A social network is made up of a set of actors (such as individuals or organizations) and a set of binary relations between these actors (such as relationships, connections, or interactions). From a social network perspective, the goal is to model the structure of a social group to identify how this structure influences other variables and how structures change over time. Semantic analysis in social media (SASM) is the semantic processing of the text messages as well as of the meta-data, in order to build intelligent applications based on social media data.
SASM helps develop automated tools and algorithms to monitor, capture, and analyze the large amounts of data collected from social media in order to predict user behavior or extract other kinds of information. If the amount of data is very large, techniques for “big data” processing need to be used, such as online algorithms that do not need to store all the data in order to update the models based on the incoming data.
In this book, we focus on the analysis of the textual data from social media, via new NLP techniques and applications. Workshops such as the EACL 2014 Workshop on Language Analysis in Social Media [Farzindar et al., 2014], the NAACL/HLT 2013 workshop on Language Analysis in Social Media [Farzindar et al., 2013], and the EACL 2012 Workshop for Semantic Analysis in Social Media [Farzindar and Inkpen, 2012] have been increasingly focusing on NLP techniques and applications that study the effect of social media messages on our daily lives, both personally and professionally.
Social media textual data is the collection of openly available texts that can be obtained publicly via blogs and micro-blogs, Internet forums, user-generated FAQs, chat, podcasts, online games, tags, ratings, and comments. Social media texts have several properties that make them different than traditional texts, because the nature of the social conversations, posted in real time. Detecting groups of topically related conversations is important for applications, as well as detection emotions, rumors, and incentives. As an example, in order to investigate youths’ experience of grief and mourning, a study applied NLP techniques to their tweets after the death of friends or family members [Patton et al., 2018]. Determining the locations mentioned in the messages or the locations of the users can also add valuable information. The texts are unstructured and are presented in many formats and written by different people in many languages and styles. Also, the typographic errors and chat slang have become increasingly prevalent on social networking sites like Facebook and Twitter. The authors are not professional writers and their postings are spread in many places on the Web, on various social media platforms.
Monitoring and analyzing this rich and continuous flow of user-generated content can yield unprecedentedly valuable information, which would not have been available from traditional media outlets. Semantic analysis of social media has given rise to the emerging discipline of big data analytics, which draws from social network analysis, machine learning, data mining, information retrieval, and natural language processing [Melville et al., 2009].
Figure 1.4 shows a framework for semantic analysis in social media. The first step is to identify issues and opportunities for collecting data from social networks. The data can be in the form of stored textual information (the big datacould be stored in large and complex databases or text files), it could be dynamic online data collection processed in real time, or it could be retrospective data collection for particular needs. The next step is the SASM pipeline, which consists of specific NLP tools for the social media analysis and data processing. Social media data is made up of large, noisy, and unstructured datasets. SASM transforms social media data to meaningful and understandable messages through social information and knowledge. Then, SASM analyzes the social media information in order to produce social media intelligence. Social media intelligence can be shared with users or presented to decision-makers to improve awareness, communication, planning, or problem solving. The presentation of analyzed data by SASM could be completed by data visualization methods.
Figure 1.4: A framework for semantic analysis in social media, where NLP tools transform the data into intelligence.
1.2 SOCIAL MEDIA APPLICATIONS
The automatic processing of social media data needs to design appropriate research methods for applications such as information extraction, automatic categorization, clustering, indexing data for information retrieval, and statistical machine translation. The sheer volume of social media data and the incredible rate at which new content is created makes monitoring, or any other meaningful manual analysis, unfeasible. In many applications, the amount of data is too large for effective real-time human evaluation and analysis of the data for a decision maker.
Social media monitoring is one of the major applications in SASM. Traditionally, media monitoring is defined as the activity of monitoring and tracking the output of the hard copy, online, and broadcast media which can be performed for a variety of reasons, including political, commercial, and scientific. The huge volume of information provided via social media networks is an important source for open intelligence. Social media make the direct contact with the target public possible. Unlike traditional news, the opinion and sentiment of authors provide an additional dimension for the social media data. The different sizes of source documents—such as a combination of multiple tweets and blogs—and content variability also render the task of analyzing social media documents difficult.
In social media, the real-time event search or event detection The search queries consider multiple dimensions, including spatial and temporal. In this case, some NLP methods such as information retrieval and summarization of social data in the form of various documents from multiple sources become important in order to support the event search and the detection of relevant information.
The semantic analysis of the meaning of a day’s or week’s worth of conversations in social networks for a group of topically related discussions or about a specific event presents the challenges of cross-language NLP tasks. Social media—related NLP methods that can extract information of interest to the analyst for preferential inclusion also lead us to domain-based applications in computational linguistics.
1.2.1 CROSS-LANGUAGE DOCUMENT ANALYSIS IN SOCIAL MEDIA DATA
The application of existing NLP techniques to social media from different languages and multiple resources faces several additional challenges; the tools for text analysis are typically designed for specific languages. The main research issue therefore lies in assessing whether language-independence or language-specificity is to be preferred. Users publish
