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[目的/意义]社会化在线评论与传统的专业性评论相比,具有更为显著的传播速度和影响力。文本评论中的情感因素并非单纯的数量化评分能够完全体现的。对本文评论中情感因素的测量与分析,能够有助于在线评论的全角度识别与揭示,更加客观准确地反映在线评论的价值。[过程/方法]通过提取用户发布的在线文本评论数据,采用有监督机器学习的算法,分别计算文本评论的情感分类得分、情感倾向得分、综合情感得分。从类型、地区、人数多个维度对情感得分与总评分进行交叉对比分析。[结果/结论]研究结果表明,文本评论蕴含的情感因素对总评分具有部分的影响作用。用户的认知偏好、社会文化背景和评论人数占比会对情感因素的有用性产生影响。 相似文献
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【目的/意义】负面在线评论已成为商家重要的经营决策信息,对了解客户消费满意度、改善产品和服务质量
具有重要意义。【方法/过程】该文将情感分析和关键词抽取相结合,提出一种基于BiGRU-CNN 和 TextRank的在
线评论负面关键词抽取方法,即首先对在线评论文本数据进行清洗,然后构建 BiGRU- CNN 情感分类模型对在
线评论进行情感分析,最后采取TextRank 方法抽取情感分析得到的负面评论中的关键词。利用这种方法,对十个
产品与服务类别的6万余条消费者在线评论文本数据进行实证分析。【结果/结论】实验结果表明,该方法能准确判
别客户负面在线评论情感倾向,F1值达92.41%,并且负面在线评论关键词抽取结果能较好帮助商家完善产品质量
和服务。【创新/局限】提出一种结合双向GRU 和CNN 结合的情感分类模型,在此基础上基于TextRank 方法抽取
情感分析得到的负面评论中的关键词,进一步提升模型对于在线评论情感分析的准确性。 相似文献
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按照人们对产品的健康影响、环境污染方面的情感认知,开发用于评估产品环境形象的人工智能方法。基于“舆情环境形象”概念,从健康影响、环境污染、环境风险和情感倾向4个方面建立产品舆情环境形象评估框架,以产品的互联网传播大数据为信息源,人工标注分类语料,采用自然语言处理技术和卷积神经网络算法训练验证分类模型。进一步以33种高风险和高污染产品为分析对象,利用从互联网获取的相关新闻、评论或公众言论进行模型分类和环境形象评估,结果表明环境舆情判定模型F值为0.91,产品的环境情感极性均以正面情绪为主,受舆情讨论热点程度影响,舆情数量多的产品情感倾向占比趋于均衡化、舆情数量少的产品情感倾向占比趋于极端化。其中,化妆品、药品的环境健康风险最高,高环境风险产品分别包括凡士林、角鲨烯和咖啡因。 相似文献
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This article describes in-depth research on machine learning methods for sentiment analysis of Czech social media. Whereas in English, Chinese, or Spanish this field has a long history and evaluation datasets for various domains are widely available, in the case of the Czech language no systematic research has yet been conducted. We tackle this issue and establish a common ground for further research by providing a large human-annotated Czech social media corpus. Furthermore, we evaluate state-of-the-art supervised machine learning methods for sentiment analysis. We explore different pre-processing techniques and employ various features and classifiers. We also experiment with five different feature selection algorithms and investigate the influence of named entity recognition and preprocessing on sentiment classification performance. Moreover, in addition to our newly created social media dataset, we also report results for other popular domains, such as movie and product reviews. We believe that this article will not only extend the current sentiment analysis research to another family of languages, but will also encourage competition, potentially leading to the production of high-end commercial solutions. 相似文献
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在线旅游社区中的用户评论客观真实地反映了游客关于旅游景点和服务的感受,本文基于在线评论数据构建了一个游客情感分析模型。该模型首先从多个知名旅游网站的评论社区中获取关于某旅游目的地的评论文本并进行预处理,利用领域本体构建方法构建旅游本体,将处理后的评论文本与旅游本体进行匹配,得出本体各属性的分类评论集,运用情感程度加权规则计算这些评论集的情感极性均值,得出游客关于旅游各要素总体情感倾向,并进行可视化分析与展示。该方法能够直观显示游客关于旅游目的地的总体情感倾向,为旅游经营者改进服务提供参考,以庐山旅游为例,验证了该模型的可行性。 相似文献
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Examining the Influence of Emotional Expressions in Online Consumer Reviews on Perceived Helpfulness
《Information processing & management》2020,57(6):102266
Drawing on the cognitive appraisal theory of emotions and the attribution theory, this study extends existing research by examining how the emotional expressions influence perceived helpfulness of online consumer reviews (OCRs). We include two negative emotions (anger, fear) and two positive emotions (pride, surprise). Each of these four emotions can be described with respect to six emotional appraisal dimensions which containing certainty, pleasantness, attentional activity, anticipated effort, control and others’ responsibility. Hypotheses thus developed were empirically validated using the laboratory experiment in the context of restaurant services. Research results indicated that emotion expressions in OCRs have an indirect effect on perceived helpfulness through attribution about the reviewer's cognitive effort. We find that reviews with negative emotions are perceived to be more reviewer's cognitive efforts than positive emotions. More specifically, OCRs with negative emotions tend to comprise more diagnostic features related the product or service, and are more informative. We further examined whether the gender of reader moderates the relationship between different emotional expressions and perceived reviewer's cognitive effort. The results find that reviews conveying positive emotions tended to have a greater impact on male readers’ perception of reviewer's cognitive effort than those of female readers. Reviews conveying negative emotions were found to have a greater impact on female readers’ perception of reviewer's cognitive effort than that of male readers. The study results add to existing knowledge of the influence of emotional expression on perceived helpfulness, which will advance our understanding of information processing in the psychological mechanisms influencing the attitude. Applying the results from this study, restaurant service providers can make different coping strategy for discrete emotions and platform administrators can assistant reviewer express their emotions more precisely. 相似文献
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[目的/意义] 随着"互联网+"在医疗服务行业的应用与发展,积累了大量的医疗评价信息,利用情感分析技术可以对其进行有效地挖掘和利用,从而为医疗管理提供决策参考。[方法/过程] 基于框架语义理论建立医疗情感语义分类词典;采用词典和规则相结合的方法进行在线医疗评论的情感语义分析,标注情感类别、情感主题、极性和强度等信息。[结果/结论] 通过在线医疗评论数据测试,验证了研究方法的有效性和科学性,是情感分析向医疗健康领域纵深发展的一次有益探索。 相似文献
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《Information processing & management》2020,57(1):102122
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions.In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis. 相似文献
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《Information processing & management》2022,59(3):102948
The research assesses local government debt risks in China with deep learning methods. We perform natural language processing and sentiment classification on all publicly available prefecture-level governments’ annual work reports from the previous three years. Then, for each of these cities, we calculated sentiment scores related to debt risks and examined the regional distribution of risks. Our empirical findings indicate that special attention should be paid to China's inland areas, where local government debt risks are highly concentrated. This paper extends the existing literature on discourse analysis with quantitative methods to the research of political economy. 相似文献
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Recently, sentiment classification has received considerable attention within the natural language processing research community. However, since most recent works regarding sentiment classification have been done in the English language, there are accordingly not enough sentiment resources in other languages. Manual construction of reliable sentiment resources is a very difficult and time-consuming task. Cross-lingual sentiment classification aims to utilize annotated sentiment resources in one language (typically English) for sentiment classification of text documents in another language. Most existing research works rely on automatic machine translation services to directly project information from one language to another. However, different term distribution between original and translated text documents and translation errors are two main problems faced in the case of using only machine translation. To overcome these problems, we propose a novel learning model based on active learning and semi-supervised co-training to incorporate unlabelled data from the target language into the learning process in a bi-view framework. This model attempts to enrich training data by adding the most confident automatically-labelled examples, as well as a few of the most informative manually-labelled examples from unlabelled data in an iterative process. Further, in this model, we consider the density of unlabelled data so as to select more representative unlabelled examples in order to avoid outlier selection in active learning. The proposed model was applied to book review datasets in three different languages. Experiments showed that our model can effectively improve the cross-lingual sentiment classification performance and reduce labelling efforts in comparison with some baseline methods. 相似文献
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《Information processing & management》2020,57(2):102179
Online review helpfulness has always sparked a heated discussion among academics and practitioners. Despite the fact that research has extensively examined the impacts of review title and content on perceptions of online review helpfulness, the underlying mechanism of how the similarities between a review' title and content may affect review helpfulness has been rarely explored. Based on mere exposure theory, a research model reflecting the influences of title-content similarity and sentiment consistency on review helpfulness was developed and empirically examined by using data collected from 127,547 product reviews on Amazon.com. The TF-IDF and the cosine of similarity were used for measuring the text similarity between review title and review content, and the Tobit model was used for regression analysis. The results showed that the title-content similarity positively affected review helpfulness. In addition, the positive effect of title-content similarity on review helpfulness is increased when the title-content sentiment consistency is high. The title sentiment also negatively moderates the impact of the title-content similarity on review helpfulness. The present research can help online retailers identify the most helpful reviews and, thus, reduce consumers' search costs as well as assist reviewers in contributing more valuable online reviews. 相似文献
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《Information processing & management》2019,56(4):1425-1438
This paper extracted discrete emotions from online reviews based on an emotion classification approach, and examined the differential effects of three discrete emotions (anger, fear, sadness) on perceived review helpfulness. We empirically tested the hypotheses by analyzing the “verified purchase” reviews on Amazon.com. The findings of this study extend the previous research by suggesting that product type moderates the effects of emotions on perceived review helpfulness. Anger embedded in a customer review exerts a greater negative impact on perceived review helpfulness for experience goods than for search goods. Fear embedded in a review is identified as an important emotional cue to positively affect the perceived review helpfulness with more persuasive messages. As the level of sadness embedded in a review increases, perceived review helpfulness decreases. These findings contribute to a better understanding of the important role of emotions embedded in reviews on the perceived review helpfulness. This study also provides practical insights related to the presentation of online reviews and gives suggestions for consumers regarding how to select and write a helpful review. 相似文献
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《Information processing & management》2023,60(2):103199
Stance is defined as the expression of a speaker's standpoint towards a given target or entity. To date, the most reliable method for measuring stance is opinion surveys. However, people's increased reliance on social media makes these online platforms an essential source of complementary information about public opinion. Our study contributes to the discussion surrounding replicable methods through which to conduct reliable stance detection by establishing a rule-based model, which we replicated for several targets independently. To test our model, we relied on a widely used dataset of annotated tweets - the SemEval Task 6A dataset, which contains 5 targets with 4,163 manually labelled tweets. We relied on “off-the-shelf” sentiment lexica to expand the scope of our custom dictionaries, while also integrating linguistic markers and using word-pairs dependency information to conduct stance classification. While positive and negative evaluative words are the clearest markers of expression of stance, we demonstrate the added value of linguistic markers to identify the direction of the stance more precisely. Our model achieves an average classification accuracy of 75% (ranging from 67% to 89% across targets). This study is concluded by discussing practical implications and outlooks for future research, while highlighting that each target poses specific challenges to stance detection. 相似文献
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《Information processing & management》2019,56(4):1245-1259
Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge amount of reviews, sentiment analysis plays a basic role to extract significant information and overall sentiment orientation of reviews. In this paper, we present a deep-learning-based method to classify a user's opinion expressed in reviews (called RNSA).To the best of our knowledge, a deep learning-based method in which a unified feature set which is representative of word embedding, sentiment knowledge, sentiment shifter rules, statistical and linguistic knowledge, has not been thoroughly studied for a sentiment analysis. The RNSA employs the Recurrent Neural Network (RNN) which is composed by Long Short-Term Memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about the word are vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the following drawbacks: words with similar semantic context but opposite sentiment polarity; contextual polarity; sentence types; word coverage limit of an individual lexicon; word sense variations. To verify the effectiveness of our work, we conduct sentence-level sentiment classification on large-scale review datasets. We obtained encouraging result. Experimental results show that (1) feature vectors in terms of (a) statistical, linguistic and sentiment knowledge, (b) sentiment shifter rules and (c) word-embedding can improve the classification accuracy of sentence-level sentiment analysis; (2) our method that learns from this unified feature set can obtain significant performance than one that learns from a feature subset; (3) our neural model yields superior performance improvements in comparison with other well-known approaches in the literature. 相似文献
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为了理解在线评论对消费者网络购买意愿影响的主要动因,基于计划行为理论、技术接受模型理论和网购顾客消费体验对在线评论行为作用模型,构建在线评论对消费者网络购买决策影响的动因模型,并提出若干假设,最后通过数据采集,采用AMOS21.0软件进行数据分析,对模型和假设进行了实证研究,统计分析结果表明: 消费者——网站关系、在线评论数量、在线评论质量、在线评论接收者专业能力、在线评论接收者涉入度、在线评论接收者感知风险影响消费者网络购买意愿,在线评论者资信度和在线评论的时效性影响不显著.基于此,本文对结果进行了讨论,并对消费者和网商营销提出了建议. 相似文献
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《Information processing & management》2020,57(5):102278
The increase in acceptability and popularity of social media has made extracting information from the data generated on social media an emerging field of research. An important branch of this field is predicting future events using social media data. This paper is focused on predicting box-office revenue of a movie by mining people's intention to purchase a movie ticket, termed purchase intention, from trailer reviews. Movie revenue prediction is important due to risks involved in movie production despite the high cost involved in the production. Previous studies in this domain focus on the use of twitter data and IMDB reviews for the prediction of movies that have already been released. In this paper, we build a model for movie revenue prediction prior to the movie's release using YouTube trailer reviews. Our model consists of novel methods of calculating purchase intention, positive-to-negative sentiment ratio, and like-to-dislike ratio for movie revenue prediction. Our experimental results prove the superiority of our approach compared to three baseline approaches and achieved a relative absolute error of 29.65%. 相似文献
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《Information processing & management》2020,57(5):102221
Although statistical learning methods have achieved success in e-commerce platform product review sentiment classification, two problems have limited its practical application: 1) The computational efficiency to process large-scale reviews; 2) the ability to continuously learn from increasing reviews and multiple domains. This paper presents a continuous naïve Bayes learning framework for large-scale and multi-domain e-commerce platform product review sentiment classification. While keeping the high computational efficiency of the traditional naïve Bayes model, we extend the parameter estimation mechanism in naïve Bayes to a continuous learning style. We furthermore propose ways to fine-tune the learned distribution based on three kinds of assumptions to better adapt to different domains. Experimental results on the Amazon product and movie review sentiment datasets show that our model can use the knowledge learned from past domains to guide learning in new domains, and has a better capacity of dealing with reviews that are continuously updated and come from different domains. 相似文献