scholarly journals Japanese Dictionary for Sentiment Analysis of Counselling Text

2021 ◽  
Author(s):  
Masataka Nakayama ◽  
Hatanaka Chihiro ◽  
Hisae Konakawa ◽  
Yuka Suzuki ◽  
Alethea Hui Qin Koh ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Text Analysis ◽  
General Public ◽  
Clinical Psychologists ◽  
Japanese Language ◽  
Valence Rating ◽  
Counselling Services ◽  
Related Text ◽  
Language Context

Chat-based counselling has become increasingly popular in the era of telecommunication. The need for accessible therapy has been exacerbated by the COVID-19 pandemic. Given its text-based nature, chat-based counselling provides an opportunity for machine-based analysis. It even has the potential to provide machine-based counselling services. However, the informational resources for machine-based analysis and interaction are rather scarce especially in a Japanese-language context. We created a Japanese dictionary for sentiment analysis, using a technique via machine-based text analysis, tailored for counselling related text. It includes 2389 words that were frequently used in chat-based counselling corpora. The following attributes were included for each word: (1) valence rating by the general public, (2) valence rating by clinical psychologists, (3) emotionality, and (4) body-relatedness.

scholarly journals Unpacking the Quantifying and Qualifying Potential of Semi-Open Job Satisfaction Questions through Computer-Aided Sentiment Analysis

2021 ◽  
Author(s):  
Indy Wijngaards ◽  
Martijn Burger ◽  
Job van Exel
Keyword(s):  
Job Satisfaction ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Discriminant Validity ◽  
Added Value ◽  
Information Richness ◽  
Convergent And Discriminant Validity ◽  
Computer Aided ◽  
Satisfaction Question ◽  
Open Question

AbstractDespite their suitability for mitigating survey biases and their potential for enhancing information richness, open and semi-open job satisfaction questions are rarely used in surveys. This is mostly due to the high costs associated with manual coding and difficulties that arise when validating text measures. Recently, advances in computer-aided text analysis have enabled researchers to rely less on manual coding to construct text measures. Yet, little is known about the validity of text measures generated by computer-aided text analysis software and only a handful of studies have attempted to demonstrate their added value. In light of this gap, drawing on a sample of 395 employees, we showed that the responses to a semi-open job satisfaction question can reliably and conveniently be converted into a text measure using two types of computer-aided sentiment analysis: SentimentR, and Linguistic Inquiry and Word Count (LIWC) 2015. Furthermore, the substantial convergence between the LIWC2015 and, in particular, SentimentR measure with a closed question measure of job satisfaction and logical associations with closed question measures of constructs that fall within and outside job satisfaction’s nomological network, suggest that a semi-open question has adequate convergent and discriminant validity. Finally, we illustrated that the responses to our semi-open question can be used to fine-tune the computer-aided sentiment analysis dictionaries and unravel antecedents of job satisfaction.

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

A Study and Comparison of Sentiment Analysis Techniques Using Demonetization

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Krishna Kumar Mohbey ◽  
Brijesh Bakariya ◽  
Vishakha Kalal
Keyword(s):  
Sentiment Analysis ◽  
Text Analysis ◽  
Analytical Approach ◽  
Document Type ◽  
Analysis Techniques

Sentiment analysis is an analytical approach that is used for text analysis. The aim of sentiment analysis is to determine the opinion and subjectivity of any opinion, review, or tweet. The aim of this chapter is to study and compare some of the techniques used to classify opinions using sentiment analysis. In this chapter, different techniques of sentiment analysis have been discussed with the case study of demonetization in India during 2016. Based on the sentiment analysis, people's opinion can be classified on different polarities such as positive, negative, or neutral. These techniques will be classified on different categories based on size of data, document type, and availability. In addition, this chapter also discusses various applications of sentiment analysis techniques in different domains.

A Survey on Sentiment Analysis Techniques for Twitter

2022 ◽  
pp. 57-90
Author(s):  
Surabhi Verma ◽  
Ankit Kumar Jain
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Analysis Techniques ◽  
Goods And Services ◽  
Text Document ◽  
The Subject ◽  
Over Time

People regularly use social media to express their opinions about a wide variety of topics, goods, and services which make it rich in text mining and sentiment analysis. Sentiment analysis is a form of text analysis determining polarity (positive, negative, or neutral) in text, document, paragraph, or clause. This chapter offers an overview of the subject by examining the proposed algorithms for sentiment analysis on Twitter and briefly explaining them. In addition, the authors also address fields related to monitoring sentiments over time, regional view of views, neutral tweet analysis, sarcasm detection, and various other tasks in this area that have drawn the researchers ' attention to this subject nearby. Within this chapter, all the services used are briefly summarized. The key contribution of this survey is the taxonomy based on the methods suggested and the debate on the theme's recent research developments and related fields.

Network Text Analysis and Sentiment Analysis

2016 ◽  
pp. 137-153
Author(s):  
Veronica Ravaglia ◽  
Luca Zanazzi ◽  
Elvis Mazzoni
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Social Networking Sites ◽  
Word Of Mouth ◽  
Network Text Analysis ◽  
Online Conversations ◽  
Web Forums

Through Social Media, like social networking sites, wikis, web forums or blogs, people can debate and influence each other. Due to this reason, the analysis of online conversations has been recognized to be relevant to organizations. In the chapter we introduce two strategic tools to monitor and analyze online conversations, Sentiment Text Analysis (STA) and Network Text Analysis (NTA). Finally, we propose one empirical example in which these tools are integrated to analyze Word-of-Mouth regarding products and services in the Digital Marketplace.

Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

2018 ◽  
Vol 9 (2) ◽  
pp. 111-120
Author(s):  
Argha Roy ◽  
Shyamali Guria ◽  
Suman Halder ◽  
Sayani Banerjee ◽  
Sourav Mandal
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Efficient Method ◽  
Text Analysis ◽  
Human Life ◽  
Analysis Techniques ◽  
Classification Technique ◽  
Textual Data ◽  
The Web

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.

Emotion Analysis from Human Voice Using Various Prosodic Features and Text Analysis

2020 ◽  
Vol 17 (9) ◽  
pp. 4244-4247
Author(s):  
Vybhav Jain ◽  
S. B. Rajeshwari ◽  
Jagadish S. Kallimani
Keyword(s):  
Emotion Recognition ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Good Accuracy ◽  
Age Groups ◽  
Speech Emotion Recognition ◽  
Prosodic Features ◽  
Voice Analysis ◽  
Emotion Analysis ◽  
Human Voice

Emotion Analysis is a dynamic field of research with the aim to provide a method to recognize the emotions of a person only from their voice. It is more famously recognized as the Speech Emotion Recognition (SER) problem. This problem has been studied upon from more than a decade with results coming from either Voice Analysis or Text Analysis. Individually, both these methods have shown a good accuracy up till now. But, the use of both of these methods in unison has showed a much more better result than either one of those parts considered individually. When different people of different age groups are talking, it is important to understand their emotions behind what they say as this will in turn help us in reacting better. To try and achieve this, the paper implements a model which performs Emotion Analysis based on both Tone and Text Analysis. The prosodic features of the tone are analyzed and then the speech is converted to text. Once the text has been extracted from the speech, Sentiment Analysis is done on the extracted text to further improve the accuracy of the Emotion Recognition.

scholarly journals Les publications japonaises sur la traduction : un aperçu

2002 ◽  
Vol 33 (1) ◽  
pp. 115-126 ◽  
Author(s):  
Daniel Gile
Keyword(s):  
Machine Translation ◽  
Language Learners ◽  
General Public ◽  
Public Speaking ◽  
Japanese Language ◽  
Translation Theory ◽  
The World ◽  
Scientific Papers ◽  
Didactic Texts ◽  
Japanese Translation

Summary Japanese publications on translation are markedly more numerous than Western publications. They are aimed at the general public rather than at professionals or academics, and few are truly scientific or academic. They deal with the Japanese context, with hardly any reference to foreign publications, authors, ideas or translation activities. They are also short-lived and disappear from bookstores and publishers' stocks within a few years. Theoretical translation texts are "philosophical" rather than scientific. Didactic texts are often aimed at language learners rather than at would-be translators. Linguistic translation texts are more interesting for the insight they give into the Japanese language and its use than for their contribution to translation theory. Texts that criticize published translations are numerous and very popular, something which is rather unique in the world. Many translation books are highly personal and contain numerous anecdotes from their authors' lives. Interpretation books are interesting, as they are more pragmatic than Western texts on the same subject, and address questions that Western publications seldom or never refer to. Machine translation articles are becoming increasingly popular. They tend to be confined to superficial explanations of the operation of systems and to descriptions of commercial products. Truly scientific papers on MT also exist, but their circulation is limited to academic and technical circles. There are a few periodicals dealing with translation. Most of the articles they carry are written by the same authors and have the same characteristics as the texts described above. On the whole, they are more interesting than translation books, as they are shorter and therefore denser. Articles on translation can also be found in countless books and periodicals on the Japanese language, on linguistics, sociology, public speaking, etc., as well as in weekly and monthly magazines and in other publications. This paper is followed by a list of Japanese texts on translation and by a list of Western language texts on translation of Japanese or on subjects relevant to the understanding of Japanese translation problems.

scholarly journals O sentimento político em redes sociais: big data, algoritmos e as emoções nos tweets sobre o impeachment de Dilma Rousseff | Political sentiment in social networks: big data, algorithims and emotions in tweets about the impeachment of Dilma Rousseff

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Fabio Malini ◽  
Patrick Ciarelli ◽  
Jean Medeiros
Keyword(s):  
Social Networks ◽  
Genetic Algorithms ◽  
Big Data ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Negative Polarization ◽  
Political Networks ◽  
Networks Analysis ◽  
Fear And Anxiety

Resumo Este artigo se propõe a ampliar a metodologia perspectivista (MALINI, 2016) de análise de redes sociais, incorporando um procedimento de análise dos sentimentos das mensagens postadas em redes de controvérsias políticas, em particular, em dois momentos distintos da campanha pelo impeachment da presidenta Dilma. O primeiro é o período da eclosão das manifestações antipetistas, no dia 15 de março de 2015. O segundo, dia 27 de agosto de 2016, quando a presidenta é deposta do cargo. Realiza uma revisão sobre a análise de sentimentos em megadados do Twitter e constrói uma metodologia que combina classificação humana de textos com aplicação de algoritmos genéticos de análise de textos, no intuito de analisar sentimentos genéricos (baseado na polarização positivo/negativos) e sentimento específicos, baseados nas seguintes emoções: Alegria, Raiva, Medo, Antecipação, Desgosto, Tristeza, Surpresa e Confiança. Conclui demonstrando que os movimentos pró e anti-Dilma são marcados pelo predomínio de sentimento de raiva, medo e ansiedade, confirmando a hipótese que a trolagem ofensiva demarca o estilo da indignação propagada em redes políticas no Twitter brasileiro.  Palavras-Chave: Análise de Sentimento; Big Data; Redes; Política; Twitter.Abstract This article aims to expand the perspectivist methodology (Malini, 2016) of social networks analysis, incorporating a proceeding of sentiment analysis of the messages posted in networks of political controversies, in particular, in two distinct moments of the campaign for the impeachment of President Dilma. The first is the period of the outbreak of PT protests, on March 15, 2015. The second, on August 27, 2016, when the president is deposed. We will be doing a theoretical review about sentiment analysis in Big Data on Twitter to build a methodology that combines human classification of texts with the application of genetic algorithms of text analysis and to analyze generic sentiments (based on positive / negative polarization) and specific sentiment, based on emotions like Joy, Anger, Fear, Anticipation, Disgust, Sadness, Surprise and Trust. It concludes by demonstrating that pro and anti-Dilma movements are marked by a predominance of anger, fear and anxiety, confirming the hypothesis that an offensive trolling demarcates the style of indignation propagated by political networks in Brazilian Twitter.Keywords: Sentiment Analysis; Big Data; Social Network; Politics; Twitter. 

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