scholarly journals SentiProdBR: Building Domain-Specific Sentiment Lexicons for the Portuguese Language

2021 ◽  
Author(s):  
Tiago de Melo
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Bayes Theorem ◽  
Experimental Results ◽  
Product Categories ◽  
Domain Specific ◽  
Alternative Approaches ◽  
The Web

Online reviews are readily available on the Web and widely used for decision-making. However, only a few studies on Portuguese sentiment analysis are reported due to the lack of resources including domain-specific sentiment lexical collections. In this paper, we present an effective methodology using probabilities of the Bayes’ Theorem for building a set of lexicons, called SentiProdBR, for 10 different product categories for the Portuguese language. Experimental results indicate that our methodology significantly outperforms several alternative approaches of building domain-specific sentiment lexicons.

Application of Sentiment Analysis in Movie reviews

2019 ◽  
pp. 77-90 ◽  
Author(s):  
ThippaReddy Gadekallu ◽  
Akshat Soni ◽  
Deeptanu Sarkar ◽  
Lakshmanna Kuruva
Keyword(s):  
Sentiment Analysis ◽  
Experimental Work ◽  
Opinion Mining ◽  
Rating Systems ◽  
Domain Specific ◽  
Analysis Process ◽  
The People ◽  
Textual Data ◽  
Feature Based ◽  
The Web

Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured, or unstructured textual data. In this chapter, the authors try to focus the task of sentiment analysis on IMDB movie review database. This chapter presents the experimental work on a new kind of domain-specific feature-based heuristic for aspect-level sentiment analysis of movie reviews. The authors have devised an aspect-oriented scheme that analyzes the textual reviews of a movie and assign it a sentiment label on each aspect. Finally, the authors conclude that incorporating syntactical information in the models is vital to the sentiment analysis process. The authors also conclude that the proposed approach to sentiment classification supplements the existing rating movie rating systems used across the web and will serve as base to future researches in this domain.

Sentiment Analysis of Arabic Documents

2021 ◽  
pp. 307-331
Author(s):  
Hichem Rahab ◽  
Mahieddine Djoudi ◽  
Abdelhafid Zitouni
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
Arabic Language ◽  
Rule Based ◽  
Arabic Speakers ◽  
European Languages ◽  
Internet Users ◽  
Arabic Sentiment Analysis ◽  
Considerable Work ◽  
The Web

Today, it is usual that a consumer seeks for others' feelings about their purchasing experience on the web before a simple decision of buying a product or a service. Sentiment analysis intends to help people in taking profit from the available opinionated texts on the web for their decision making, and business is one of its challenging areas. Considerable work of sentiment analysis has been achieved in English and other Indo-European languages. Despite the important number of Arabic speakers and internet users, studies in Arabic sentiment analysis are still insufficient. The current chapter vocation is to give the main challenges of Arabic sentiment together with their recent proposed solutions in the literature. The chapter flowchart is presented in a novel manner that obtains the main challenges from presented literature works. Then it gives the proposed solutions for each challenge. The chapter reaches the finding that the future tendency will be toward rule-based techniques and deep learning, allowing for more dealings with Arabic language inherent characteristics.

Post-visit and pre-visit tourist destination image through eWOM sentiment analysis and perceived helpfulness

2016 ◽  
Vol 28 (11) ◽  
pp. 2609-2627 ◽  
Author(s):  
M. Rosario González-Rodríguez ◽  
Rocio Martínez-Torres ◽  
Sergio Toral
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Point Of View ◽  
Destination Image ◽  
Tourist Destination ◽  
Content Type ◽  
Sentiment Orientation ◽  
The City ◽  
Perceived Helpfulness ◽  
The Web

Purpose This paper aims to explore the image of travel destinations after the visit by analysing sentiment orientation of the online reviews, and how this orientation, as well as other electronic word of mouth (eWOM)’s credibility sources, can affect the perceived helpfulness of shared opinions measured through the helpfulness score. Design/methodology/approach Tourist destinations are increasingly affected by travel-related information shared through the Web. More and more people first check the previous travel experiences of other people to build their own destination image and to help them in their choice of destination. This paper analyses the shared opinions related to the city of Barcelona in a well-known eWOM website. The reviewers’ opinion and the credibility sources of eWOM are extracted from the web using a webscraper, while the sentiment score to analyse the discourse orientation (positive vs negative) is calculated using computer-based sentiment analysis techniques. Findings Online reviews’ users are reluctant to provide extreme polar opinions (very negative, very positive) to any travel subcategory (hotel, restaurant, attractions and night-life) of a tourist destination. The results obtained also reveal that eWOM’s perceived helpfulness grows with the expertise of the reviewer. However, the helpfulness score given to the reviews posted is not influenced by the sentiment orientation of the author’s opinion. Research limitations/implications This research is limited to the case study of Ciao, which is a well-known consumer platform, and the city of Barcelona, which is a top touristic destination. However, the approach proposed can be easily extended to other similar consumer platforms and cities using the same methodology. Practical implications Understanding the information posted in the media environment is a major concern in the field of marketing destination planning. Positive and negative eWOM offers potential consumers a clear picture on the tourist destination, and this information can be used by Destination Marketing Organisations to meet customers’ needs and expectations. The perceived helpfulness of reviews analysed in this paper can also help practitioners and scholars to understand those factors that make reviews more trustable. Originality/value From a methodological point of view, the main contribution of this research is the utilisation of an unstructured approach to the measurement of the destination image based on the sentiment analysis of shared opinions. From a theoretical point of view, the study relates the post-visit destination image with the pre-visit image formation process, using the sentiment orientation of the former and the perceived helpfulness of the latter.

Sentimental Analysis of Online Reviews Using Fuzzy Sets and Rough Sets

2017 ◽  
pp. 376-394
Author(s):  
Anuradha Jagadeesan ◽  
Amit Patil
Keyword(s):  
Fuzzy Sets ◽  
Sentiment Analysis ◽  
Rough Sets ◽  
Opinion Mining ◽  
Online Reviews ◽  
Purchase Decisions ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Excellent Source ◽  
The Web

With the increased interest of online users in E-commerce, the web has become an excellent source for buying and selling of products online. Customer reviews on the web help potential customers to make purchase decisions, and for manufacturers to incorporate improvements in their product or develop new marketing strategies. The increase in customer reviews of a product influence the popularity and the sale rate of the product. This lead to a very important question about the analysis of the sentiments (opinions) expressed in the reviews. As such internet does not have any quality control over customer reviews and it could vary in terms of its quality. Also the trustworthiness of the online reviews is debatable. Sentiment Analysis (SA) or Opinion Mining is the computational analysis of opinions, sentiments, emotions and subjectivity of text. In this chapter, we take a look at the various research challenges and a new dimension involved in sentiment analysis using fuzzy sets and rough sets.

scholarly journals Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
C. Sitaula ◽  
A. Basnet ◽  
A. Mainali ◽  
T. B. Shahi
Keyword(s):  
Feature Extraction ◽  
Sentiment Analysis ◽  
Extraction Methods ◽  
The Novel ◽  
Domain Specific ◽  
The Social ◽  
Stable Performance ◽  
Media Platform ◽  
The Web

COVID-19 has claimed several human lives to this date. People are dying not only because of physical infection of the virus but also because of mental illness, which is linked to people’s sentiments and psychologies. People’s written texts/posts scattered on the web could help understand their psychology and the state they are in during this pandemic. In this paper, we analyze people’s sentiment based on the classification of tweets collected from the social media platform, Twitter, in Nepal. For this, we, first, propose to use three different feature extraction methods—fastText-based (ft), domain-specific (ds), and domain-agnostic (da)—for the representation of tweets. Among these three methods, two methods (“ds” and “da”) are the novel methods used in this study. Second, we propose three different convolution neural networks (CNNs) to implement the proposed features. Last, we ensemble such three CNNs models using ensemble CNN, which works in an end-to-end manner, to achieve the end results. For the evaluation of the proposed feature extraction methods and CNN models, we prepare a Nepali Twitter sentiment dataset, called NepCOV19Tweets, with 3 classes (positive, neutral, and negative). The experimental results on such dataset show that our proposed feature extraction methods possess the discriminating characteristics for the sentiment classification. Moreover, the proposed CNN models impart robust and stable performance on the proposed features. Also, our dataset can be used as a benchmark to study the COVID-19-related sentiment analysis in the Nepali language.

scholarly journals The impact of semantics on aspect level opinion mining

2021 ◽  
Vol 7 ◽  
pp. e558
Author(s):  
Eman M. Aboelela ◽  
Walaa Gad ◽  
Rasha Ismail
Keyword(s):  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Opinion Mining ◽  
Online Shopping ◽  
Experimental Results ◽  
Proposed Model ◽  
Online Comments ◽  
The Impact ◽  
F Measure ◽  
The Web

Recently, many users prefer online shopping to purchase items from the web. Shopping websites allow customers to submit comments and provide their feedback for the purchased products. Opinion mining and sentiment analysis are used to analyze products’ comments to help sellers and purchasers decide to buy products or not. However, the nature of online comments affects the performance of the opinion mining process because they may contain negation words or unrelated aspects to the product. To address these problems, a semantic-based aspect level opinion mining (SALOM) model is proposed. The SALOM extracts the product aspects based on the semantic similarity and classifies the comments. The proposed model considers the negation words and other types of product aspects such as aspects’ synonyms, hyponyms, and hypernyms to improve the accuracy of classification. Three different datasets are used to evaluate the proposed SALOM. The experimental results are promising in terms of Precision, Recall, and F-measure. The performance reaches 94.8% precision, 93% recall, and 92.6% f-measure.

scholarly journals Rethinking Defeasible Reasoning: A Scalable Approach

2020 ◽  
Vol 20 (4) ◽  
pp. 552-586
Author(s):  
MICHAEL J. MAHER ◽  
ILIAS TACHMAZIDIS ◽  
GRIGORIS ANTONIOU ◽  
STEPHEN WADE ◽  
LONG CHENG
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Sensor Networks ◽  
Experimental Results ◽  
Defeasible Reasoning ◽  
Social Media Analytics ◽  
Technological Advances ◽  
Large Numbers ◽  
Complex Knowledge ◽  
The Web

AbstractRecent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks, and social media. Analytics in terms of defeasible reasoning – for example, for decision making – could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.

scholarly journals Exploring the Impact of Linguistic Signals Transmission on Patients’ Health Consultation Choice: Web Mining of Online Reviews

2021 ◽  
Vol 18 (19) ◽  
pp. 9969
Author(s):  
Adnan Muhammad Shah ◽  
Mudassar Ali ◽  
Abdul Qayyum ◽  
Abida Begum ◽  
Heesup Han ◽  
...  
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
Signaling Theory ◽  
Online Reviews ◽  
Treatment Choice ◽  
Quality Of Information ◽  
Physician Rating ◽  
Sentic Computing ◽  
Negative Sentiment

Background: Patients face difficulties identifying appropriate physicians owing to the sizeable quantity and uneven quality of information in physician rating websites. Therefore, an increasing dependence of consumers on online platforms as a source of information for decision-making has given rise to the need for further research into the quality of information in the form of online physician reviews (OPRs). Methods: Drawing on the signaling theory, this study develops a theoretical model to examine how linguistic signals (affective signals and informative signals) in physician rating websites affect consumers’ decision making. The hypotheses are tested using 5521 physicians’ six-month data drawn from two leading health rating platforms in the U.S (i.e., Healthgrades.com and Vitals.com) during the COVID-19 pandemic. A sentic computing-based sentiment analysis framework is used to implicitly analyze patients’ opinions regarding their treatment choice. Results: The results indicate that negative sentiment, review readability, review depth, review spelling, and information helpfulness play a significant role in inducing patients’ decision-making. The influence of negative sentiment, review depth on patients’ treatment choice was indirectly mediated by information helpfulness. Conclusions: This paper is a first step toward the understanding of the linguistic characteristics of information relating to the patient experience, particularly the emerging field of online health behavior and signaling theory. It is also the first effort to our knowledge that employs sentic computing-based sentiment analysis in this context and provides implications for practice.

Simulation of carding condensing process

2021 ◽  
pp. 004051752098812
Author(s):  
Xixi Qian ◽  
Yuanying Shen ◽  
Qiaoli Cao ◽  
Jun Ruan ◽  
Chongwen Yu
Keyword(s):  
Experimental Results ◽  
Simulation Results ◽  
Carding Machine ◽  
The Web

A simulation describing the fiber movement during the condensation was conducted, and the effect of the condensation in the carding machine was studied. The simulation results showed that the condensation has the blending and the evening effect on the condensed sliver, which can be explained by the fiber rearrangement. Moreover, the increasing web width and the decreasing condensing length can result in a more uniform sliver. Further, the evening effect of the web width on the web was verified by experiments. The simulation results were in general agreement with the experimental results.

scholarly journals Thirty Years of Human Rights Study in the Web of Science Database (1990–2020)

2021 ◽  
Vol 18 (4) ◽  
pp. 2131
Author(s):  
Priscilla Paola Severo ◽  
Leonardo B. Furstenau ◽  
Michele Kremer Sott ◽  
Danielli Cossul ◽  
Mariluza Sott Bender ◽  
...  
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Human Rights ◽  
Network Analysis ◽  
Reproductive Health ◽  
Web Of Science ◽  
Health Issues ◽  
Human Beings ◽  
Field Of Study ◽  
The Web

The study of human rights (HR) is vital in order to enhance the development of human beings, but this field of study still needs to be better depicted and understood because violations of its core principles still frequently occur worldwide. In this study, our goal was to perform a bibliometric performance and network analysis (BPNA) to investigate the strategic themes, thematic evolution structure, and trends of HR found in the Web of Science (WoS) database from 1990 to June 2020. To do this, we included 25,542 articles in the SciMAT software for bibliometric analysis. The strategic diagram produced shows 23 themes, 12 of which are motor themes, the most important of which are discussed in this article. The thematic evolution structure presented the 21 most relevant themes of the 2011–2020 period. Our findings show that HR research is directly related to health issues, such as mental health, HIV, and reproductive health. We believe that the presented results and HR panorama presented have the potential to be used as a basis on which researchers in future works may enhance their decision making related to this field of study.

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