Combining granularity-based topic-dependent and topic-independent evidences for opinion detection

Fouille des opinion, une sous-discipline dans la recherche d'information (IR) et la linguistique computationnelle, fait référence aux techniques de calcul pour l'extraction, la classification, la compréhension et l'évaluation des opinions exprimées par diverses sources de nouvelles en ligne, social commentaires des médias, et tout autre contenu généré par l'utilisateur. Il est également connu par de nombreux autres termes comme trouver l'opinion, la détection d'opinion, l'analyse des sentiments, la classification sentiment, de détection de polarité, etc. Définition dans le contexte plus spécifique et plus simple, fouille des opinion est la tâche de récupération des opinions contre son besoin aussi exprimé par l'utilisateur sous la forme d'une requête. Il y a de nombreux problèmes et défis liés à l'activité fouille des opinion. Dans cette thèse, nous nous concentrons sur quelques problèmes d'analyse d'opinion. L'un des défis majeurs de fouille des opinion est de trouver des opinions concernant spécifiquement le sujet donné (requête). Un document peut contenir des informations sur de nombreux sujets à la fois et il est possible qu'elle contienne opiniâtre texte sur chacun des sujet ou sur seulement quelques-uns. Par conséquent, il devient très important de choisir les segments du document pertinentes à sujet avec leurs opinions correspondantes. Nous abordons ce problème sur deux niveaux de granularité, des phrases et des passages. Dans notre première approche de niveau de phrase, nous utilisons des relations sémantiques de WordNet pour trouver cette association entre sujet et opinion. Dans notre deuxième approche pour le niveau de passage, nous utilisons plus robuste modèle de RI i.e. la language modèle de se concentrer sur ce problème. L'idée de base derrière les deux contributions pour l'association d'opinion-sujet est que si un document contient plus segments textuels (phrases ou passages) opiniâtre et pertinentes à sujet, il est plus opiniâtre qu'un document avec moins segments textuels opiniâtre et pertinentes. La plupart des approches d'apprentissage-machine basée à fouille des opinion sont dépendants du domaine i.e. leurs performances varient d'un domaine à d'autre. D'autre part, une approche indépendant de domaine ou un sujet est plus généralisée et peut maintenir son efficacité dans différents domaines. Cependant, les approches indépendant de domaine souffrent de mauvaises performances en général. C'est un grand défi dans le domaine de fouille des opinion à développer une approche qui est plus efficace et généralisé. Nos contributions de cette thèse incluent le développement d'une approche qui utilise de simples fonctions heuristiques pour trouver des documents opiniâtre. Fouille des opinion basée entité devient très populaire parmi les chercheurs de la communauté IR. Il vise à identifier les entités pertinentes pour un sujet donné et d'en extraire les opinions qui leur sont associées à partir d'un ensemble de documents textuels. Toutefois, l'identification et la détermination de la pertinence des entités est déjà une tâche difficile. Nous proposons un système qui prend en compte à la fois l'information de l'article de nouvelles en cours ainsi que des articles antérieurs pertinents afin de détecter les entités les plus importantes dans les nouvelles actuelles. En plus de cela, nous présentons également notre cadre d'analyse d'opinion et tâches relieés. Ce cadre est basée sur les évidences contents et les évidences sociales de la blogosphère pour les tâches de trouver des opinions, de prévision et d'avis de classement multidimensionnel. Cette contribution d'prématurée pose les bases pour nos travaux futurs. L'évaluation de nos méthodes comprennent l'utilisation de TREC 2006 Blog collection et de TREC Novelty track 2004 collection. La plupart des évaluations ont été réalisées dans le cadre de TREC Blog track.Opinion mining is a sub-discipline within Information Retrieval (IR) and Computational Linguistics. It refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online sources like news articles, social media comments, and other user-generated content. It is also known by many other terms like opinion finding, opinion detection, sentiment analysis, sentiment classification, polarity detection, etc. Defining in more specific and simpler context, opinion mining is the task of retrieving opinions on an issue as expressed by the user in the form of a query. There are many problems and challenges associated with the field of opinion mining. In this thesis, we focus on some major problems of opinion mining

Effectiveness gain of polarity detection through topic domains

National audienceMost of the work on polarity detection consists in finding out negative or positive words in a document using sentiment lexical resources. Indeed, some versions of such approaches have performed well but most of these approaches rely only on prior polarity of words and do not exploit the contextual polarity of words. Sentiment semantics of a term vary from one domain to another. For example, the word "unpredictable" conveys a positive feeling about a movie plot, but the same word conveys negative feeling in context of operating of a digital camera. In this work, we demonstrate this aspect of sentiment polarity. We use TREC Blog 2006 Data collection with topics of TREC Blog 2006 and 2007 for experimentation. The results of our experiments showed an improvement (95%) on polarity detection. The conclusion is that the context plays a role on the polarity of each word

Opinion mining: Reviewed from word to document level

International audienceOpinion mining is one of the most challenging tasks of the field of information retrieval. Research community has been publishing a number of articles on this topic but a significant increase in interest has been observed during the past decade especially after the launch of several online social networks. In this paper, we provide a very detailed overview of the related work of opinion mining. Following features of our review make it stand unique among the works of similar kind: (1) it presents a very different perspective of the opinion mining field by discussing the work on different granularity levels (like word, sentences, and document levels) which is very unique and much required, (2) discussion of the related work in terms of challenges of the field of opinion mining, (3) document level discussion of the related work gives an overview of opinion mining task in blogosphere, one of most popular online social network, and (4) highlights the importance of online social networks for opinion mining task and other related sub-tasks

Stacked Ensemble Model for Tropical Cyclone Path Prediction

Tropical cyclones (TC) are intense circular storms that cause significant economic and human losses in the coastal areas of the equatorial region. Various statistical models have been proposed to forecast the potential path of TC. This study proposes a stacked ensemble-based method to enhance the effectiveness of predicting TC paths using temporal data. The proposed method can be divided into two phases. In the first phase, the Long Short-Term Memory Networks (LSTM) and Gated Recurrent Unit (GRU) models are optimized with stacked layers to determine the most effective configuration for Stacked LSTM and Stacked GRU. In the second phase, k-fold cross-validation is employed to construct multiple Stacked LSTM and Stacked GRU models, and a Meta learner is used to ensemble the predictions from these models. We evaluate the performance of our proposed model using the temporal China Meteorological Administration (CMA) dataset and compare its results with those obtained from other ensemble and non-ensemble techniques. The results demonstrate a significant reduction in mean square error and variance achieved by the proposed model. The code is available on GitHub: TC path prediction©2023 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

PARENTAL ENGAGEMENT IN ONLINE LEARNING FOR UNIVERSITY STUDENTS DURING COVID-19

In this essay, we have emphasized the educational practices of the students during COVID-19 and evaluated the parents' role in enhancing their children's learning attitude. Four hundred individuals from various educational institutions provided information, which was then processed for SPSS data analysis. We initially evaluated the questionnaire's consistency and discovered that it was better. When we later calculated some summary statistics, we saw that most queries displayed normality. According to chi-square analysis, it was found that parents with higher educations helped their kids with their online learning—high-speed internet service. Parents with education helped their kids manage their time so they could study online with a positive attitude. They supported the purchase of the online learning equipment financially and encouraged their children to change their behavior towards it

Scientific papers citation analysis using textual features and SMOTE resampling techniques

Abstract Ascertaining the impact of research is significant for the research community and academia of all disciplines. The only prevalent measure associated with the quantification of research quality is the citation-count. Although a number of citations play a significant role in academic research, sometimes citations can be biased or made to discuss only the weaknesses and shortcomings of the research. By considering the sentiment of citations and recognizing patterns in text can aid in understanding the opinion of the peer research community and will also help in quantifying the quality of research articles. Efficient feature representation combined with machine learning classifiers has yielded significant improvement in text classification. However, the effectiveness of such combinations has not been analyzed for citation sentiment analysis. This study aims to investigate pattern recognition using machine learning models in combination with frequency-based and prediction-based feature representation techniques with and without using Synthetic Minority Oversampling Technique (SMOTE) on publicly available citation sentiment dataset. Sentiment of citation instances are classified into positive, negative or neutral. Results indicate that the Extra tree classifier in combination with Term Frequency-Inverse Document Frequency achieved 98.26% accuracy on the SMOTE-balanced dataset

Systematic Review and Usability Evaluation Covid-19 Mobile Applications in a Developing Nation

Mobile phone technology made tremendous progress today. In this current ongoing Coronavirus disease (COVID-19) pandemic situation worldwide, mobile phones have an essential role due to its number of applications. To save lives through proper guidelines and information, it is necessary to retain the people up to date. That's why well-needed applications are required that aim to decline the rampant increase of COVID-19 cases country-wide. Today with the increasing pandemic Covid-19, several applications have been developed to inform people about safety measures and keep them updated with the public health measures. COVID-19 applications relate to large public, hence, usability of these applications has far larger impact than any other type of applications which generally have a specific user group. Therefore, in this article, a systematic review is taken by considering their range of functions, target user groups, name, languages, size, user ratings, available interfaces, response time, release date, up-dating date and up to date cases. There exist several methods for evaluating usability. In this work, we use user-based usability evaluation methods to explore usability issues in COVID-19 apps. By performing analysis on the extracted data, we examine some facts like the impact of gender on usability, impact of age, usability dimensions, and the effect of app functions on usability to see whether they affect usability positively or negativel

Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network

This study was supported by the China University of Petroleum-Beijing and Fundamental Research Funds for Central Universities under Grant no. 2462020YJRC001.Peer reviewedPublisher PD

Combinaison des sources des évidences indépendantes et dépendantes de requêtes basées sur la granularité

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF

Probabilistic opinion models based on subjective sources

This article describes approaches for searching opinionated documents for a given query from a standard data collection. To detect if a text is opinionated (i.e., contain subjective information) or not, we propose two methods: the first method is based on lexicons of subjective words (i.e., SentiWordNet) supported by the assumption that more a document contains the subjective terms more it has the tendency of being an opinionated document while the second method is based on probabilistic model supporting the idea that given a document having a strong similarity with a reference opinionated text is more likely to be opinionated. In the second method, we take support of language modeling approach to compute this similarity. Experiments are conducted with TREC Blog06 as the test collection and the IMDB data collection as being the reference data collection. The experimental results report effectiveness of both methods