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4 pages, 163 KiB

Open AccessEditorial

Sentiment Analysis for Social Media

by Carlos A. Iglesias and Antonio Moreno

Appl. Sci. 2019, 9(23), 5037; https://doi.org/10.3390/app9235037 - 22 Nov 2019

Cited by 42 | Viewed by 6949

Abstract

Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, [...] Read more.

Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, such as deep learning techniques, have solved some of the traditional challenges in the area caused by the scarcity of lexical resources. In this Special Issue, different approaches that advance this discipline are presented. The contributed articles belong to two broad groups: technological contributions and applications. Full article

(This article belongs to the Special Issue Sentiment Analysis for Social Media)

Research

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26 pages, 9746 KiB

Open AccessArticle

An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data

by Hyoji Ha, Hyunwoo Han, Seongmin Mun, Sungyun Bae, Jihye Lee and Kyungwon Lee

Appl. Sci. 2019, 9(12), 2419; https://doi.org/10.3390/app9122419 - 13 Jun 2019

Cited by 21 | Viewed by 6967

Abstract

This paper suggests a method for refining a massive amount of collective intelligence data and visualizing it with a multilevel sentiment network in order to understand the relevant information in an intuitive and semantic way. This semantic interpretation method minimizes network learning in [...] Read more.

This paper suggests a method for refining a massive amount of collective intelligence data and visualizing it with a multilevel sentiment network in order to understand the relevant information in an intuitive and semantic way. This semantic interpretation method minimizes network learning in the system as a fixed network topology only exists as a guideline to help users understand. Furthermore, it does not need to discover every single node to understand the characteristics of each clustering within the network. After extracting and analyzing the sentiment words from the movie review data, we designed a movie network based on the similarities between the words. The network formed in this way will appear as a multilevel sentiment network visualization after the following three steps: (1) design a heatmap visualization to effectively discover the main emotions on each movie review; (2) create a two-dimensional multidimensional scaling (MDS) map of semantic word data to facilitate semantic understanding of network and then fix the movie network topology on the map; (3) create an asterism graphic with emotions to allow users to easily interpret node groups with similar sentiment words. The research also presents a virtual scenario about how our network visualization can be used as a movie recommendation system. We next evaluated our progress to determine whether it would improve user cognition for multilevel analysis experience compared to the existing network system. Results showed that our method provided improved user experience in terms of cognition. Thus, it is appropriate as an alternative method for semantic understanding. Full article

(This article belongs to the Special Issue Sentiment Analysis for Social Media)

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14 pages, 388 KiB

Open AccessArticle

Sentiment Classification Using Convolutional Neural Networks

by Hannah Kim and Young-Seob Jeong

Appl. Sci. 2019, 9(11), 2347; https://doi.org/10.3390/app9112347 - 7 Jun 2019

Cited by 152 | Viewed by 13019

Abstract

As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits [...] Read more.

As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models. Full article

(This article belongs to the Special Issue Sentiment Analysis for Social Media)

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10 pages, 579 KiB

Open AccessArticle

Using Social Media to Identify Consumers’ Sentiments towards Attributes of Health Insurance during Enrollment Season

by Eline M. van den Broek-Altenburg and Adam J. Atherly

Appl. Sci. 2019, 9(10), 2035; https://doi.org/10.3390/app9102035 - 17 May 2019

Cited by 22 | Viewed by 5223

Abstract

This study aims to identify sentiments that consumers have about health insurance by analyzing what they discuss on Twitter. The objective was to use sentiment analysis to identify attitudes consumers express towards health insurance and health care providers. We used an Application Programming [...] Read more.

This study aims to identify sentiments that consumers have about health insurance by analyzing what they discuss on Twitter. The objective was to use sentiment analysis to identify attitudes consumers express towards health insurance and health care providers. We used an Application Programming Interface to gather tweets from Twitter with the words “health insurance” or “health plan” during health insurance enrollment season in the United States in 2016‒2017. Word association was used to find words associated with “premium,” “access,” “network,” and “switch.” Sentiment analysis established which specific emotions were associated with insurance and medical providers, using the NRC Emotion Lexicon, identifying emotions. We identified that provider networks, prescription drug benefits, political preferences, and norms of other consumers matter. Consumers trust medical providers but they fear unexpected health events. The results suggest that there is a need for different algorithms to help consumers find the plans they want and need. Consumers buying health insurance in the Affordable Care Act marketplaces in the United States choose lower-cost plans with limited benefits, but at the same time express fear about unexpected health events and unanticipated costs. If we better understand the origin of the sentiments that drive consumers, we may be able to help them better navigate insurance plan options and insurers can better respond to their needs. Full article

(This article belongs to the Special Issue Sentiment Analysis for Social Media)

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28 pages, 4093 KiB

Open AccessArticle

Personality or Value: A Comparative Study of Psychographic Segmentation Based on an Online Review Enhanced Recommender System

by Hui Liu, Yinghui Huang, Zichao Wang, Kai Liu, Xiangen Hu and Weijun Wang

Appl. Sci. 2019, 9(10), 1992; https://doi.org/10.3390/app9101992 - 15 May 2019

Cited by 35 | Viewed by 19770

Abstract

Big consumer data promises to be a game changer in applied and empirical marketing research. However, investigations of how big data helps inform consumers’ psychological aspects have, thus far, only received scant attention. Psychographics has been shown to be a valuable market segmentation [...] Read more.

Big consumer data promises to be a game changer in applied and empirical marketing research. However, investigations of how big data helps inform consumers’ psychological aspects have, thus far, only received scant attention. Psychographics has been shown to be a valuable market segmentation path in understanding consumer preferences. Although in the context of e-commerce, as a component of psychographic segmentation, personality has been proven to be effective for prediction of e-commerce user preferences, it still remains unclear whether psychographic segmentation is practically influential in understanding user preferences across different product categories. To the best of our knowledge, we provide the first quantitative demonstration of the promising effect and relative importance of psychographic segmentation in predicting users’ online purchasing preferences across different product categories in e-commerce by using a data-driven approach. We first construct two online psychographic lexicons that include the Big Five Factor (BFF) personality traits and Schwartz Value Survey (SVS) using natural language processing (NLP) methods that are based on behavior measurements of users’ word use. We then incorporate the lexicons in a deep neural network (DNN)-based recommender system to predict users’ online purchasing preferences considering the new progress in segmentation-based user preference prediction methods. Overall, segmenting consumers into heterogeneous groups surprisingly does not demonstrate a significant improvement in understanding consumer preferences. Psychographic variables (both BFF and SVS) significantly improve the explanatory power of e-consumer preferences, whereas the improvement in prediction power is not significant. The SVS tends to outperform BFF segmentation, except for some product categories. Additionally, the DNN significantly outperforms previous methods. An e-commerce-oriented SVS measurement and segmentation approach that integrates both BFF and the SVS is recommended. The strong empirical evidence provides both practical guidance for e-commerce product development, marketing and recommendations, and a methodological reference for big data-driven marketing research. Full article

(This article belongs to the Special Issue Sentiment Analysis for Social Media)

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17 pages, 443 KiB

Open AccessArticle

Classification of Cyber-Aggression Cases Applying Machine Learning

by Guadalupe Obdulia Gutiérrez-Esparza, Maite Vallejo-Allende and José Hernández-Torruco

Appl. Sci. 2019, 9(9), 1828; https://doi.org/10.3390/app9091828 - 2 May 2019

Cited by 28 | Viewed by 5640

Abstract

The adoption of electronic social networks as an essential way of communication has become one of the most dangerous methods to hurt people’s feelings. The Internet and the proliferation of this kind of virtual community have caused severe negative consequences to the welfare [...] Read more.

The adoption of electronic social networks as an essential way of communication has become one of the most dangerous methods to hurt people’s feelings. The Internet and the proliferation of this kind of virtual community have caused severe negative consequences to the welfare of society, creating a social problem identified as cyber-aggression, or in some cases called cyber-bullying. This paper presents research to classify situations of cyber-aggression on social networks, specifically for Spanish-language users of Mexico. We applied Random Forest, Variable Importance Measures (VIMs), and OneR to support the classification of offensive comments in three particular cases of cyber-aggression: racism, violence based on sexual orientation, and violence against women. Experimental results with OneR improve the comment classification process of the three cyber-aggression cases, with more than 90% accuracy. The accurate classification of cyber-aggression comments can help to take measures to diminish this phenomenon. Full article

(This article belongs to the Special Issue Sentiment Analysis for Social Media)

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14 pages, 310 KiB

Open AccessArticle

Sentiment-Aware Word Embedding for Emotion Classification

by Xingliang Mao, Shuai Chang, Jinjing Shi, Fangfang Li and Ronghua Shi

Appl. Sci. 2019, 9(7), 1334; https://doi.org/10.3390/app9071334 - 29 Mar 2019

Cited by 34 | Viewed by 6980

Abstract

Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integrating sentiment evidence within the emotional embedding component of a term vector. We take [...] Read more.

Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integrating sentiment evidence within the emotional embedding component of a term vector. We take advantage of the multiple types of emotional knowledge, just as the existing emotional lexicon, to build emotional word vectors to represent emotional information. Then the emotional word vector is combined with the traditional word embedding to construct the hybrid representation, which contains semantic and emotional information as the inputs of the emotion classification experiments. Our method maintains the interpretability of word embeddings, and leverages external emotional information in addition to input text sequences. Extensive results on several machine learning models show that the proposed methods can improve the accuracy of emotion classification tasks. Full article

(This article belongs to the Special Issue Sentiment Analysis for Social Media)

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12 pages, 847 KiB

Open AccessArticle

Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum

by Sunghee Park and Jiyoung Woo

Appl. Sci. 2019, 9(6), 1249; https://doi.org/10.3390/app9061249 - 25 Mar 2019

Cited by 35 | Viewed by 6668

Abstract

Sentiment analysis is the most common text classification tool that analyzes incoming messages and tells whether the underlying sentiment is positive, negative, or neutral. We can use this technique to understand people by gender, especially people who are suffering from a sensitive disease. [...] Read more.

Sentiment analysis is the most common text classification tool that analyzes incoming messages and tells whether the underlying sentiment is positive, negative, or neutral. We can use this technique to understand people by gender, especially people who are suffering from a sensitive disease. People use health-related web forums to easily access health information written by and for non-experts and also to get comfort from people who are in a similar situation. The government operates medical web forums to provide medical information, manage patients’ needs and feelings, and boost information-sharing among patients. If we can classify people’s emotional or information needs by gender, age, or location, it is possible to establish a detailed health policy specialized into patient segments. However, people with sensitive illness such as AIDS tend to hide their information. Especially, in the case of sexually transmitted AIDS, we can detect problems and needs according to gender. In this work, we present a gender detection model using sentiment analysis and machine learning including deep learning. Through the experiment, we found that sentiment features generate low accuracy. However, senti-words give better results with SVM. Overall, traditional machine learning algorithms have a high misclassification rate for the female category. The deep learning algorithm overcomes this drawback with over 90% accuracy. Full article

(This article belongs to the Special Issue Sentiment Analysis for Social Media)

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16 pages, 1277 KiB

Open AccessArticle

A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets

by Mohammed Jabreel and Antonio Moreno

Appl. Sci. 2019, 9(6), 1123; https://doi.org/10.3390/app9061123 - 17 Mar 2019

Cited by 125 | Viewed by 11060

Abstract

Currently, people use online social media such as Twitter or Facebook to share their emotions and thoughts. Detecting and analyzing the emotions expressed in social media content benefits many applications in commerce, public health, social welfare, etc. Most previous work on sentiment and [...] Read more.

Currently, people use online social media such as Twitter or Facebook to share their emotions and thoughts. Detecting and analyzing the emotions expressed in social media content benefits many applications in commerce, public health, social welfare, etc. Most previous work on sentiment and emotion analysis has only focused on single-label classification and ignored the co-existence of multiple emotion labels in one instance. This paper describes the development of a novel deep learning-based system that addresses the multiple emotion classification problem in Twitter. We propose a novel method to transform it to a binary classification problem and exploit a deep learning approach to solve the transformed problem. Our system outperforms the state-of-the-art systems, achieving an accuracy score of 0.59 on the challenging SemEval2018 Task 1:E-cmulti-label emotion classification problem. Full article

(This article belongs to the Special Issue Sentiment Analysis for Social Media)

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