Sentiment Analysis in Social Networks

Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu

• Publisher: Morgan Kaufmann
• Year: 2016
• ISBN:
  9780128044384
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking.

Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature.

Further, this volume:

• Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies
• Provides insights into opinion spamming, reasoning, and social network analysis
• Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences
• Serves as a one-stop reference for the state-of-the-art in social media analytics

• Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies
• Provides insights into opinion spamming, reasoning, and social network mining
• Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences
• Serves as a one-stop reference for the state-of-the-art in social media analytics

Chapter 1

Challenges of Sentiment Analysis in Social Networks

An Overview

F.A. Pozzi^a; E. Fersini^b; E. Messina^b; B. Liu^{c a} SAS Institute Srl, Milan, Italy
^b University of Milano-Bicocca, Milan, Italy
^c University of Illinois at Chicago, Chicago, IL, United States

Abstract

In this chapter we provide some background knowledge for the sentiment analysis research field, subsequently providing an overview of the current challenges related to the social network environment. The main content of the chapter is devoted to introducing the reader to some preliminary concepts, which are further detailed in the subsequent chapters.

Keywords

Sentiment analysis; Opinion mining; Social networks; Objective sentences; Subjective sentences; Explicit opinions; Implicit opinions

1 Background

Sentiment analysis, which is also called opinion mining, has been one of the most active research areas in natural language processing since early 2000 [1]. The aim of sentiment analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, so as to create structured and actionable knowledge to be used by either a decision support system or a decision maker.

Unsurprisingly, there has been some confusion among researchers about the difference between sentiment and opinion, thus debating whether the field should be called sentiment analysis or opinion mining. In Merriam-Webster’s Collegiate Dictionary, sentiment is defined as an attitude, thought, or judgment prompted by feeling, whereas opinion is defined as a view, judgment, or appraisal formed in the mind about a particular matter. The difference is quite subtle, and each of them contains some elements of the other. The definitions indicate that an opinion is more of a person’s concrete view about something, whereas a sentiment is more of a feeling. For example, the sentence “I am concerned about the current political situation” expresses a sentiment, whereas the sentence “I think politics is not doing well” expresses an opinion. If someone says the first sentence in a conversation, we can respond by saying “I share your sentiment,” but for the second sentence we would normally say “I agree/disagree with you.” However, the underlying meanings of the two sentences are strictly related because the sentiment depicted in the first sentence is likely to be a feeling caused by the opinion in the second sentence. Conversely, the first sentiment sentence implies a negative opinion about politics, which is what the second sentence is saying. Although in most cases opinions imply positive or negative sentiments, some opinions do not, such as “I think he will win at the next presidential election.”

More formally, as defined in [1], an opinion is a quintuple,

$\begin{array}{l}\hfill (e_i,a_{ij},s_{ijkl},h_k,t_l),\end{array}$

(1.1)

where e_i is the name of an entity, a_ij is an aspect of e_i, s_ijkl is the sentiment on aspect a_ij of entity e_i, h_k denotes the opinion holder, and t_l is the time when the opinion is expressed by h_k.

The sentiment s_ijkl is positive, negative, or neutral, or expressed with different strength/intensity levels, such as the 1–5 stars system used by most review websites (eg, Amazon¹).

For example, consider that yesterday John bought an iPhone. He tested it during the whole day and when he went home from work (at 19:00 on 2-15-2014) he wrote on his favorite social network the message “The iPhone is very good, but they still need to work on battery life and security issues.” Let us index “iPhone,” “battery life,” and “security” as 1, 2, and 3 respectively. John is indexed as 4 and the time when he wrote the sentence is indexed as 5. Then John is the opinion holder h₄ and t₅ (“19:00 2-15-2014”) is the time when the opinion is expressed by h₄ (John). The term “iPhone” is the entity e₁, “battery life” and “security issues” are aspects a₁₂ and a₁₃ of entity e₁ (“iPhone”), s₁₂₄₅ = neg is the sentiment on aspect a₁₂ (“battery life”) of entity e₁ (“iPhone”). and s₁₃₄₅ = neg is the sentiment on aspect a₁₃ (“security issues”) of entity e₁ (“iPhone’). When an opinion is on the entity itself as a whole, the special aspect “GENERAL” is used to denote it.

From the definition of sentiment analysis reported above, “the aim of sentiment analysis is therefore to define automatic tools able to extract subjective information in order to create structured and actionable knowledge.” In line with this, the quintuple-based definition provides a framework to transform unstructured text to structured data (eg, a database table). Then a rich set of qualitative, quantitative, and trend analyses can be performed with traditional database management systems and online analytical processing tools.

Because of the importance of sentiment analysis to business and society, it has spread from computer science to management science and the social sciences. In recent years industrial activities surrounding sentiment analysis have also thrived: numerous start-ups have emerged, and many large corporations have built their own in-house capabilities (eg, Microsoft, Google, Hewlett-Packard, IBM, SAP, and SAS Global Communications).

Thanks to its strong applicability and interest in both the academic field and the industrial field, sentiment analysis is nowadays a trending topic. Fig. 1.1 represents the Google Trends data related to the keywords sentiment analysis, clearly demonstrating the continuous and increasing interest in this field.

Fig. 1.1 Google Trends data related to the keywords sentiment analysis.

Nowadays, sentiment analysis has gained even more value with the advent of social networks. Their great diffusion and their role in modern society represent one of the most interesting novelties in recent years, capturing the interest of researchers, journalists, companies, and governments. The dense interconnection that often arises among active users generates a discussion space that is able to motivate and involve individuals of a larger agora, linking people with common objectives and facilitating diverse forms of collective action. Social networks are therefore creating a digital revolution, enabling the expression and spread of emotions and opinions through the network, opening a window on others’ respective worlds, and snooping into their lives. Opinionated data on the net, if properly collected and analyzed, allow one not only to understand and explain many complex social phenomena but also to predict them.

Considering that nowadays the current technological progress enables the efficient storing and retrieval of a huge amount of data, the current focus is now on methods for extracting information and creating knowledge from raw sources. Social networks represent an emerging challenging sector in the context of big data: the natural language expressions of people can be easily reported through short text messages, rapidly creating unique content of huge dimensions that must be efficiently and effectively analyzed to create actionable knowledge for decision making processes.

The massive quantity of continuously contributing texts in social networks, which should be processed in real time so as to make informed decisions, calls for two main types of radical progress: (1) a change of direction in the research through the transition from a data-constrained to data-enabled paradigm and (2) the convergence to a multidisciplinary area that mainly takes advantage of psychology, sociology, natural language processing, and machine learning. The knowledge embedded in social network content has been shown to be of paramount importance from both user and company/organization points of view: while people express opinions on any kind of topic in an unconstrained and unbiased environment, corporations and institutions can gauge valuable information from raw sources. To make quali...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Editors’ Biographies
• Preface
• Acknowledgments
• Chapter 1: Challenges of Sentiment Analysis in Social Networks: An Overview
• Chapter 2: Beyond Sentiment: How Social Network Analytics Can Enhance Opinion Mining and Sentiment Analysis
• Chapter 3: Semantic Aspects in Sentiment Analysis
• Chapter 4: Linked Data Models for Sentiment and Emotion Analysis in Social Networks
• Chapter 5: Sentic Computing for Social Network Analysis
• Chapter 6: Sentiment Analysis in Social Networks: A Machine Learning Perspective
• Chapter 7: Irony, Sarcasm, and Sentiment Analysis
• Chapter 8: Suggestion Mining From Opinionated Text
• Chapter 9: Opinion Spam Detection in Social Networks
• Chapter 10: Opinion Leader Detection
• Chapter 11: Opinion Summarization and Visualization
• Chapter 12: Sentiment Analysis With SpagoBI
• Chapter 13: SOMA: The Smart Social Customer Relationship Management Tool: Handling Semantic Variability of Emotion Analysis With Hybrid Technologies
• Chapter 14: The Human Advantage: Leveraging the Power of Predictive Analytics to Strategically Optimize Social Campaigns
• Chapter 15: Price-Sensitive Ripples and Chain Reactions: Tracking the Impact of Corporate Announcements With Real-Time Multidimensional Opinion Streaming
• Chapter 16: Conclusion and Future Directions
• Author Index
• Subject Index