Exploring sentiment parsing of microblogging texts for opinion polling on chinese public figures

Microblogging websites such as twitter and Sina Weibo have attracted many users to share their experiences and express their opinions on a variety of topics, making them ideal platforms on which to conduct electronic opinion polls on products, services and public figures. However, conventional sentiment analysis methods for microblogging messages may not meet the demands of opinion polls for public figures. Therefore, in this study, we focus mainly on the problem of sentiment analysis for opinion polling on Chinese public figures. We propose a sentiment parsing-based architecture, which represents and labels opinion targets and their corresponding sentiments jointly to avoid the mismatching of them, for opinion poll of public figures using microblogs. Furthermore, we formulate sentiment parsing of microblogging sentences as a sequence labeling problem and adapt different Recurrent Neural Network (RNN) models to train and infer the model. Our experimental results demonstrate that the proposed sentiment parsing-based methods achieve better performance than conventional sentiment score-based methods in opinion polling on public figures using microblogs.     

A multidimensional data model using the fuzzy model based on the semantic translation

With the rapid development of Web 2.0 sites such as Blogs and Wikis users are encouraged to express opinions about certain products, services or social topics over the web. There is a method for aggregating these opinions, called Opinion Aggregation, which is made up of four steps: Collect, Identify, Classify and Aggregate. In this paper, we present a new conceptual multidimensional data model based on the Fuzzy Model based on the Semantic Translation to solve the Aggregate step of an Opinion Aggregation architecture, which allows exploiting the measure values resulting from integrating heterogeneous information (including unstructured data such as free texts) by means of traditional Business Intelligence tools. We also present an entire Opinion Aggregation architecture that includes the Aggregate step and solves the rest of steps (Collect, Identify and Classify) by means an Extraction, Transformation and Loading process. This architecture has been implemented in an Oracle Relational Database Management System. We have applied it to integrate heterogeneous data extracted from certain high end hotels websites, and we show a case study using the collected data during several years in the websites of high end hotels located in Granada (Spain). With this integrated information, the Data Warehouse user can make several analyses with the benefit of an easy linguistic interpretability and a high precision by means of interactive tools such as the dashboards.     

Feature-based opinion mining through ontologies

The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public’s opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users’ opinions. However, current opinion mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative opinion mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based opinion mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches.     

基于浅层句法特征的评价对象抽取研究

随着网络评论文本数量的快速增长,文本情感分析越来越受到研究者的广泛关注. 句子级文本情感分析就是对主观性文本进行细粒度的挖掘,有重要的研究价值. 评论句中的评价对象抽取是句子级情感分析要研究的关键问题之一. 为了提高评价对象抽取的性能,本文提出在系统模型的训练过程中引入浅层句法信息和启发式位置信息,同时在不增加领域词典的情况下, 有效提高系统的精确率.实验结果表明,将本文提出的特征引入到条件随机域模型和对比模型后,系统的各项指标均有所提高, 并且条件随机域模型的结果优于对比模型.同时,将条件随机域模型的结果与2008年国内中文评测的最大值比较,其F值超过最大值 5%.     

SumView: A Web-based engine for summarizing product reviews and customer opinions

In this paper, we develop SumView, a Web-based review summarization system, to automatically extract the most representative expressions and customer opinions in the reviews on various product features. Different from existing review analysis which makes more efforts on sentiment classification and opinion mining, our system mainly focuses on summarization, i.e., delivering the majority of information contained in the review documents by selecting the most representative review sentences for each extracted product feature. Comprehensive case studies and experiments demonstrate the effectiveness of our system, and the user study shows users’ satisfaction.     

Understanding corporate data models

Corporate data models are widely used to support data management within organisations. However, both IS professionals and business users find them difficult to understand. This paper describes a methodology for designing and representing corporate data models that uses explanation and visualisation mechanisms to improve understanding, and reports a case study of the use of the methodology in the development of a data warehouse. The methodology was shown to be effective in that a high quality corporate data model was designed and then understood and utilised by all the participants. The model was used as an active, hypertext interface to the first prototype of the data warehouse. The case study findings indicated that: scenarios are useful for eliciting information requirements and explaining abstract concepts in the model to business users; graphical icons and subject area partitions are effective means of visualising the model and lead to improved understanding of the model by business users; and design rationale is an effective means of explaining the evolution of concepts in the model for specialist data modellers.     

中文网络评论的产品特征提取及情感倾向判定

随着互联网和电子商务的发展,用户在购买或使用商品之后会在网络站点上发表对产品的评论,大量的产品评论中所包含的丰富信息,可以为生产厂商和用户提供重要的决策依据。基于文本的语义和语言分析,提出了从产品评论中提取用户关注的产品特征的方法,并根据用户的关注程度对产品特征进行排序;同时,根据观点词的极性值判定用户对产品特征的情感倾向以及情感倾向强度。本研究采用从互联网上获得的针对笔记本电脑的产品评论作为实验对象,实验结果初步证明该方法具有良好的准确率和召回率。     

Ontology-based sentiment analysis of twitter posts

The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.     

Riding the tide of sentiment change: sentiment analysis with evolving online reviews

The last decade has seen a rapid growth in the volume of online reviews. A great deal of research has been done in the area of opinion mining, aiming at analyzing the sentiments expressed in those reviews towards products and services. Most of the such work focuses on mining opinions from a collection of reviews posted during a particular period, and does not consider the change in sentiments when the collection of reviews evolve over time. In this paper, we fill in this gap, and study the problem of developing adaptive sentiment analysis models for online reviews. Given the success of latent semantic modeling techniques, we propose two adaptive methods to capture the evolving sentiments. As a case study, we also investigate the possibility of using the extracted adaptive patterns for sales prediction. Our proposal is evaluated on an IMDB dataset consisting of reviews of selected movies and their box office revenues. Experimental results show that the adaptive methods can capture sentiment changes arising from newly available reviews, which helps greatly improve the prediction accuracy.     

Feature-based opinion mining and ranking

The proliferation of blogs and social networks presents a new set of challenges and opportunities in the way information is searched and retrieved. Even though facts still play a very important role when information is sought on a topic, opinions have become increasingly important as well. Opinions expressed in blogs and social networks are playing an important role influencing everything from the products people buy to the presidential candidate they support. Thus, there is a need for a new type of search engine which will not only retrieve facts, but will also enable the retrieval of opinions. Such a search engine can be used in a number of diverse applications like product reviews to aggregating opinions on a political candidate or issue. Enterprises can also use such an engine to determine how users perceive their products and how they stand with respect to competition. This paper presents an algorithm which not only analyzes the overall sentiment of a document/review, but also identifies the semantic orientation of specific components of the review that lead to a particular sentiment. The algorithm is integrated in an opinion search engine which presents results to a query along with their overall tone and a summary of sentiments of the most important features.     

Expert Recommendations Based on Opinion Mining of User‐Generated Product Reviews

In this article, we introduce the idea of expert recommendations whose objective is to relate review comments with users’ tasks or expectations. We propose to use fine‐grained information such as opinions and suggestions extracted using natural language processing techniques from user reviews about products, to improve a recommendation system. While typical recommender systems compare a user profile with some reference characteristics to rate unseen items, they rarely make use of the content of reviews that users have provided on a given product. In this article, we present the application of an opinion extraction system to extract opinions and suggestions from the content of the reviews, the use of the results to compare other products with the reviewed one, and eventually the recommendation of better products to the user. The recommendations are given a confidence weight by using a trust social network.     

连锁销售数据仓库系统的构建技术的研究

数据仓库是在数据库基础上发展起来的，主要用于OLAP和数据挖掘，使企业和用户可方便地从大量隐藏的数据中抽取出潜在的、有价值的信息。文章首先提出了数据仓库系统的体系结构，然后创建了连锁销售数据仓库，实现了系统功能模块的设计与开发，最后对该系统的应用前景进行了展望。     

Integrated microblog sentiment analysis from users’ social interaction patterns and textual opinions

Traditional post-level opinion classification methods usually fail to capture a person’s overall sentiment orientation toward a topic from his/her microblog posts published for a variety of themes related to that topic. One reason for this is that the sentiments connoted in the textual expressions of microblog posts are often obscure. Moreover, a person’s opinions are often influenced by his/her social network. This study therefore proposes a new method based on integrated information of microblog users’ social interactions and textual opinions to infer the sentiment orientation of a user or the whole group regarding a hot topic. A Social Opinion Graph (SOG) is first constructed as the data model for sentiment analysis of a group of microblog users who share opinions on a topic. This represents their social interactions and opinions. The training phase then uses the SOGs of training sets to construct Sentiment Guiding Matrix (SGM), representing the knowledge about the correlation between users’ sentiments, Textual Sentiment Classifier (TSC), and emotion homophily coefficients of the influence of various types of social interaction on users’ mutual sentiments. All of these support a high-performance social sentiment analysis procedure based on the relaxation labeling scheme. The experimental results show that the proposed method has better sentiment classification accuracy than the textual classification and other integrated classification methods. In addition, IMSA can reduce pre-annotation overheads and the influence from sampling deviation.     

Learning from context: A mutual reinforcement model for Chinese microblog opinion retrieval

This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexicon-based approaches employed online public sentiment resources to rank sentimentwords relying on the document features. However, this approach could not be effectively applied to microblogs that have typical user-generated content with valuable contextual information: “user–user” interpersonal interactions and “user–post/comment” intrapersonal interactions. This contextual information is very helpful in estimating the strength of sentiment words more accurately. In this study, we integrate the social contextual relationships among users, posts/comments, and sentiment words into a mutual reinforcement model and propose a unified three-layer heterogeneous graph, on which a random walk sentiment word weighting algorithm is presented to measure the strength of opinion of the sentiment words. Furthermore, the weights of sentiment words are incorporated into a lexicon-based model for Chinese microblog opinion retrieval. Comparative experiments are conducted on a Chinese microblog corpus, and the results show that our proposed mutual reinforcement model achieves significant improvement over previous methods.     

Survey on mining subjective data on the web

In the past years we have witnessed Sentiment Analysis and Opinion Mining becoming increasingly popular topics in Information Retrieval and Web data analysis. With the rapid growth of the user-generated content represented in blogs, wikis and Web forums, such an analysis became a useful tool for mining the Web, since it allowed us to capture sentiments and opinions at a large scale. Opinion retrieval has established itself as an important part of search engines. Ratings, opinion trends and representative opinions enrich the search experience of users when combined with traditional document retrieval, by revealing more insights about a subject. Opinion aggregation over product reviews can be very useful for product marketing and positioning, exposing the customers’ attitude towards a product and its features along different dimensions, such as time, geographical location, and experience. Tracking how opinions or discussions evolve over time can help us identify interesting trends and patterns and better understand the ways that information is propagated in the Internet. In this study, we review the development of Sentiment Analysis and Opinion Mining during the last years, and also discuss the evolution of a relatively new research direction, namely, Contradiction Analysis. We give an overview of the proposed methods and recent advances in these areas, and we try to layout the future research directions in the field.     

A novel deterministic approach for aspect-based opinion mining in tourism products reviews

This work proposes an extension of Bing Liu’s aspect-based opinion mining approach in order to apply it to the tourism domain. The extension concerns with the fact that users refer differently to different kinds of products when writing reviews on the Web. Since Liu’s approach is focused on physical product reviews, it could not be directly applied to the tourism domain, which presents features that are not considered by the model. Through a detailed study of on-line tourism product reviews, we found these features and then model them in our extension, proposing the use of new and more complex NLP-based rules for the tasks of subjective and sentiment classification at the aspect-level. We also entail the task of opinion visualization and summarization and propose new methods to help users digest the vast availability of opinions in an easy manner. Our work also included the development of a generic architecture for an aspect-based opinion mining tool, which we then used to create a prototype and analyze opinions from TripAdvisor in the context of the tourism industry in Los Lagos, a Chilean administrative region also known as the Lake District. Results prove that our extension is able to perform better than Liu’s model in the tourism domain, improving both Accuracy and Recall for the tasks of subjective and sentiment classification. Particularly, the approach is very effective in determining the sentiment orientation of opinions, achieving an F-measure of 92% for the task. However, on average, the algorithms were only capable of extracting 35% of the explicit aspect expressions, using a non-extended approach for this task. Finally, results also showed the effectiveness of our design when applied to solving the industry’s specific issues in the Lake District, since almost 80% of the users that used our tool considered that our tool adds valuable information to their business.     

A public and large-scale expert information fusion method and its application: Mining public opinion via sentiment analysis and measuring public dynamic reliability

With the rapid development of social media, reliable information released by the public on social media can provide important decision-making support. Therefore, the consideration of the public as another decision-making body participating in large-scale group decision-making (LSGDM) problems has become an extensively researched topic. However, the participation of the public as a decision-making body with decision-making experts faces several issues, such as the acquisition of public opinion, the reliability of public opinion, the integration of public and expert opinions, etc. Given this, this paper proposes a public and large-scale expert information fusion method that considers public dynamic reliability via sentiment analysis and intuitionistic fuzzy number (IFN) expressions. First, sentiment analysis technology is used to process public social media data and obtain IFNs as the opinions of the public decision-making body. Second, the concept of public dynamic reliability is defined to measure the degree of integration of public opinion. Third, a novel information entropy measure of IFNs is proposed, and a new method is introduced to determine the criteria weights under the two different decision-making bodies. Finally, an optimization model that considers the consensus levels of expert subgroups is proposed to determine the weights of different decision-making bodies. The public and expert opinions are then aggregated to obtain collective decision-making information. A case study is proposed to illustrate the application of the proposed method, and the comparative analysis reveals the features and advantages of this model.     

网络意见挖掘、摘要与检索研究综述

网络上带有人的主观感情色彩的评论性文本反映了人们的意见、态度和立场,因而具有很大的利用价值.信息挖掘技术针对这些主观文本进行处理,获得有用的意见、结论和知识.首先介绍了意见挖掘出现的背景和应用意义,然后从词汇情感极性识别、粗粒度的情感分类、细粒度的意见挖掘与摘要、意见检索和相关语言资源与系统5个方面综述了研究历程和现状,最后总结了研究难点与研究趋势.     

Blog mining-review and extensions: “From each according to his opinion”

Blogs provide a type of website that contains information and personal opinions of the individual authors. The purpose of this paper is to review some of the literature aimed at gathering opinion, sentiment and information from blogs. This paper also extends the previous literature in a number of directions, extending the use of knowledge from tags on blogs, finding the need for domain specific terms to capture a richer understanding of mood of a blog and finding a relationship between information in message boards and blogs. The relationship between blog chatter and sales, and blogs and public image are also examined.     

Analyzing sentiments in Web 2.0 social media data in Chinese: experiments on business and marketing related Chinese Web forums

Web 2.0 has brought a huge amount of user-generated, social media data that contains rich information about people’s opinions and ideas towards various products, services, and ongoing social and political events. Nowadays, many companies start to look into and try to leverage this new type of data to understand their customers in order to make better business strategies and services. As a nation with rapid economic growth in recently years, China has become visible and started to play an important role in the global business and economy. Also, with the large number of Chinese Internet users, a considerable amount of options about Chinese business and market have been expressed in social media sites. Thus, it will be of interest to explore and understand those user-generated contents in Chinese. In this study, we develop an integrated framework to analyze user sentiments from Chinese social media sites by leveraging sentiment analysis techniques. Based on the framework, we conduct experiments on two popular Chinese Web forums, both related to business and marketing. By utilizing Elastic Net together with a rich body of feature representations, we achieve the highest F-measures of 84.4 and 86.7 % for the two data sets, respectively. We also demonstrate the interpretability of Elastic Net by discussing the top-ranked features with positive or negative sentiments.