50 research outputs found

    Personalized Recommendation Model: An Online Comment Sentiment Based Analysis

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    Traditional recommendation algorithms measure users’ online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Users’ reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the fine-grained analysis of emotions expressed in users’ online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts feature-sentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user feature-sentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy

    Fine-Grained Emotion Analysis Based on Mixed Model for Product Review

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    Nowadays, with the rapid development of B2C e-commerce and the popularity of online shopping, the Web storages huge number of product reviews comment by customers. A large number of reviews made it difficult for manufacturers or potential customers to track the comments and suggestions that customers made. This paper presents a method for extracting emotional elements containing emotional objects and emotional words and their tendencies from product reviews based on mixed model. First we constructed conditional random fields to extract emotional elements, lead-in semantic and word meaning as features to improve the robustness of feature template and used rules for hierarchical filtering errors. Then we constructed support vector machine to classify the emotional tendency of the fine-grained elements to achieve key information from product reviews. Deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure

    A Decision Method for Online Purchases Considering Dynamic Information Preference Based on Sentiment Orientation Classification and Discrete DIFWA Operators

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    © 2013 IEEE. Online reviews are crucial for evaluating product features and supporting consumers' purchase decisions. However, as a result of online buying behaviors, consumer habits, and discrete dynamic distribution characteristics of online reviews, and consumers typically randomly choose a limited number of reviews from discrete time frames among all reviews and give more weight to recent review information and less weight to earlier information to support their online purchase decisions; moreover, existing studies have ignored the discrete random dynamic characteristics and dynamic information preferences of consumers. To address this issue, this paper proposes a method based on sentiment orientation classification and discrete DIFWA (DDIFWA) operators for online purchase decisions considering dynamic information preferences. In this method, we transformed review texts from original discrete time slices to discrete random features, extracted product features based on the constructed feature and sentiment dictionaries, and matched pairs of features and sentiment phrases in the dictionaries. We subsequently employed three types of semantic orientation by defining semantic rules to extract the product features of each review. Of note, the semantic orientations were transformed from discrete time to semantic intuitionistic fuzzy numbers and semantic intuitionistic fuzzy information matrixes. Furthermore, we proposed two DDIFWA operators to aggregate the dynamic semantic intuitionistic fuzzy information. Specifically, we obtained the rankings of alternative products and their features to support consumers' purchase decisions using the intuitionistic fuzzy scoring function and the 'vertical projection distance' method. Finally, comparisons and experiments are provided to demonstrate the plausibility of our methods

    Leverage Business Analytics and OWA to Recommend Appropriate Projects in Crowdfunding Platform

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    Nowadays, crowdfunding is becoming more and more popular. Many studies have been published on the crowdfunding platform from different perspectives. However, among all these studies, few are concerned about the recommendation methods, which, in effect, are highly beneficial to crowdfunding websites and the participants. Having considered the situation talked above, this paper works out the several features from the relative projects of user’s current browsing project. Then we give different weights to each feature based on selective attention phenomenon, and adopt the method of OWA operator to calculate the final score of each relative project and accomplish our model by picking out the four projects with different outstanding characteristics. Finally, according to the statistics on China’s famous crowdfunding website, we conducted a group of contrast experiments and eventually testified that our proposed model could, to some extent, help classify and give recommendation effectively. Furthermore, the results of this research can give guidance to the management of crowdfunding websites and they are also very significant advices for the future crowdfunding website development

    Frame semantics for the field of climate change : d iscovering frames based on chinese and english terms

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    La plupart des dictionnaires spécialisés de termes environnementaux en mandarin sont des dictionnaires papier, compilés et révisés il y a plus de dix ans, et contiennent principalement des termes nominaux. Les informations terminologiques se limitent aux connaissances véhiculées par le terme et son ou ses équivalents anglais. Pour les lecteurs qui souhaitent connaître les propriétés sémantiques ou syntaxiques des termes et pour les lecteurs qui veulent voir l’usage des termes dans des contextes réels de textes spécialisés, les informations fournies par les dictionnaires existants sont insuffisantes. Dans cette recherche, nous avons compilé une ressource terminologique en ligne du mandarin, décrivant les termes verbaux chinois dans le domaine du changement climatique. Cette ressource comble certaines des lacunes des dictionnaires environnementaux mandarin existants, en révélant le(s) sens du terme à travers la(les) structure(s) actantielle(s) et en montrant, à travers des contextes annotés, les propriétés sémantiques et syntaxiques du terme ainsi que ses usages pratiques dans des textes spécialisés. Cette ressource répondra mieux aux besoins du public. La base théorique qui sous-tend cette recherche est la Sémantique des cadres (Fillmore, 1976, 1977, 1982, 1985; Fillmore & Atkins, 1992), et le FrameNet construit à partir de celle-ci. L’objectif principal de cette recherche est de découvrir et de définir des cadres sémantiques chinois dans le domaine du changement climatique, et d’établir des relations entre les cadres chinois définis. Les cadres sémantiques chinois sont découverts à l’aide de la méthodologie du dictionnaire environnemental multilingue DiCoEnviro (et de sa ressource d’accompagnement Framed DiCoEnviro) (L’Homme, 2018; L’Homme et al., 2020). Afin de rendre cette méthodologie applicable à une langue sino-tibétaine, le chinois, nous avons modifié et adapté cette méthodologie pour qu’elle convienne à la description des termes chinois et à la définition des cadres sémantiques chinois. Certaines de ces modifications et adaptations sont basées sur le Chinese FrameNet (CFN) (Liu & You, 2015). Afin de découvrir les cadres sémantiques chinois, un corpus monolingue en chinois mandarin sur le changement climatique (MCCC) a d’abord été compilé. Ce corpus contient 224 textes iv authentiques chinois spécialisés dans le domaine du changement climatique, qui totalisent 1,228,333 caractères chinois, soit 547,592 mots chinois. Puis, les termes candidats ont été automatiquement extraits du MCCC à l’aide du logiciel de gestion et d’analyse de corpus – Sketch Engine. Après une analyse et une validation manuelle, nous avons déterminé quels termes candidats sont des termes réels. Par la suite, la structure actancielle de chaque terme a été écrite en analysant les contextes où le terme apparaît. Ensuite, chaque sens d’un terme polysémique a été placé dans une entrée séparée et 16-20 contextes ont été sélectionnés pour chaque entrée. Puis, chaque contexte a été annoté en fonction de trois couches – structure sémantique, fonction syntaxique et groupe syntaxique. Ensuite, les termes ont été classés en fonction des scénarios qu’ils évoquent. Les termes qui dépeignent la même scène ou situation dans le domaine du changement climatique, qui ont une structure actantielle similaire et qui partagent la majorité des circonstants sont classés dans un seul cadre sémantique (critères basés sur le projet DiCoEnviro (L’Homme, 2018; L’Homme et al., 2020)). Après avoir identifié les cadres sémantiques chinois, chaque cadre a été défini. Enfin, les cadres chinois découverts ont été reliés selon les huit types de relations entre cadres proposés par Ruppenhofer et al. (2016). Pour être affichés en ligne, les entrées de termes et les cadres sémantiques ont été encodés dans des fichiers XML. Guidés par cette méthodologie de recherche, nous avons finalement relevé 23 cadres sémantiques chinois et nous les avons définis. Le résultat final de cette recherche est une ressource terminologique en chinois mandarin basée sur des cadres et spécialisée dans le domaine du changement climatique. Cette ressource terminologique se compose de deux parties. La première partie est la description d’un total de 39 termes verbaux chinois. Chaque sens d’un terme verbal polysémique étant placé dans une entrée séparée, il y a au total 59 entrées (chaque entrée contient la structure actantielle et les contextes annotés). Au total, 1,027 contextes ont été annotés. La deuxième partie de cette ressource présente les 23 cadres sémantiques chinois identifiés ainsi que les relations entre les cadres.Most of the existing Mandarin Chinese specialised dictionaries of environmental terms are paper dictionaries, compiled and revised more than ten years ago, and contain mainly noun terms. Terminological information is restricted to knowledge conveyed by the term and its English equivalent(s). For readers who want to learn about semantic or syntactic properties of terms and for readers who want to see usage of terms in real contexts of specialised texts, information provided in existing dictionaries is insufficient. In this research, we compiled an online Mandarin Chinese terminological resource, describing Chinese verb terms in the field of climate change. This resource makes up for some of the deficiencies of existing Chinese environmental dictionaries, revealing meaning(s) of the term through actantial structure(s) and showing, through annotated contexts, semantic and syntactic properties of the term as well as its practical usages in specialised texts. This resource better meets the needs of the audience. The theoretical basis underpinning this research is Frame Semantics (Fillmore, 1976, 1977, 1982, 1985; Fillmore & Atkins, 1992), and the FrameNet built from it. The main objective of this research is to discover and define Chinese semantic frames in the field of climate change, and to establish relations between the Chinese frames defined. The Chinese semantic frames are discovered with the help of the methodology of the multilingual environmental dictionary DiCoEnviro (and its accompanying resource Framed DiCoEnviro) (L’Homme, 2018; L’Homme et al., 2020). In order to make this methodology applicable to a Sino-Tibetan language, Chinese, we modified and adapted this methodology to suit the description of Chinese terms and definition of Chinese semantic frames. Some of the changes and adaptations are based on the Chinese FrameNet (CFN) (Liu & You, 2015). In order to discover Chinese semantic frames, a monolingual Mandarin (Chinese) Climate Change Corpus (MCCC) was first compiled. This corpus contains 224 authentic Chinese specialised texts in the field of climate change, totaling 1,228,333 Chinese characters, which is 547,592 Chinese words. Following this, candidate terms were automatically extracted from MCCC using the corpus ii management and analysing software – Sketch Engine. After manual analysis and validation, which of the candidate terms are true terms was clarified. Subsequently, the actantial structure of each term was written by analysing the contexts where the term occurs. Next, each sense of a polysemous term was placed in a separate entry and 16-20 contexts were selected for each entry. Then, each context was annotated in terms of three layers – semantic structure, syntactic function and syntactic group. After this, the terms were classified according to the scenarios they evoke. Terms that depict the same scene or situation in the field of climate change, have similar actantial structure, and share the majority of circumstants are categorised into one semantic frame (criteria based on the project DiCoEnviro (L’Homme, 2018; L’Homme et al., 2020)). After Chinese semantic frames were identified, each frame was defined. Finally, the discovered Chinese frames were linked according to the eight types of frame relations proposed by Ruppenhofer et al. (2016). To be displayed online, term entries and semantic frames were encoded in XML files. Guided by this research methodology, we eventually discovered and defined 23 Chinese semantic frames. The end result of this research is a frame-based Mandarin Chinese terminological resource specialised in the field of climate change. This terminological resource consists of two parts. The first part is the description of a total of 39 Chinese verb terms. With each meaning of a polysemous verb term placed in a separate entry, there are a total of 59 entries (each entry contains the actantial structure and annotated contexts). A total of 1,027 contexts were annotated. The second part of this resource presents the 23 Chinese semantic frames identified as well as the relations between frames

    SSentiaA: A Self-Supervised Sentiment Analyzer for Classification From Unlabeled Data

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    In recent years, supervised machine learning (ML) methods have realized remarkable performance gains for sentiment classification utilizing labeled data. However, labeled data are usually expensive to obtain, thus, not always achievable. When annotated data are unavailable, the unsupervised tools are exercised, which still lag behind the performance of supervised ML methods by a large margin. Therefore, in this work, we focus on improving the performance of sentiment classification from unlabeled data. We present a self-supervised hybrid methodology SSentiA (Self-supervised Sentiment Analyzer) that couples an ML classifier with a lexicon-based method for sentiment classification from unlabeled data. We first introduce LRSentiA (Lexical Rule-based Sentiment Analyzer), a lexicon-based method to predict the semantic orientation of a review along with the confidence score of prediction. Utilizing the confidence scores of LRSentiA, we generate highly accurate pseudo-labels for SSentiA that incorporates a supervised ML algorithm to improve the performance of sentiment classification for less polarized and complex reviews. We compare the performances of LRSentiA and SSSentA with the existing unsupervised, lexicon-based and self-supervised methods in multiple datasets. The LRSentiA performs similarly to the existing lexicon-based methods in both binary and 3-class sentiment analysis. By combining LRSentiA with an ML classifier, the hybrid approach SSentiA attains 10%–30% improvements in macro F1 score for both binary and 3-class sentiment analysis. The results suggest that in domains where annotated data are unavailable, SSentiA can significantly improve the performance of sentiment classification. Moreover, we demonstrate that using 30%–60% annotated training data, SSentiA delivers similar performances of the fully labeled training dataset
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