The measurement of meaning

Despite hospitality and tourism researchers’ recent attempts on examining different aspects of online word-of-mouth [WOM], its impact on hotel sales remains largely unknown in the existing literature. To fill this void, we conduct a study to empirically investigate the impact of online consumer-generated reviews on hotel room sales. Utilizing data collected from the largest travel website in China, we develop a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings. Our results indicate a significant relationship between online consumer reviews and business performance of hotels.

The impact of online user reviews on hotel room sales [Summary]

Multivariate Granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries

Academy of Management Review : 抄録雑誌の概要

A taxonomy that classifies ensemble models in the literature is presented.Surface and deep features integration is explored to improve classification.Several ensembles of classifiers and features are proposed and evaluated.Performance of the proposed models is evaluated on several sentiment datasets. Deep learning techniques for Sentiment Analysis have become very popular. They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods). Traditional surface approaches are based on complex manually extracted features, and this extraction process is a fundamental question in feature driven methods. These long-established approaches can yield strong baselines, and their predictive capabilities can be used in conjunction with the arising deep learning methods. In this paper we seek to improve the performance of deep learning techniques integrating them with traditional surface approaches based on manually extracted features. The contributions of this paper are sixfold. First, we develop a deep learning based sentiment classifier using a word embeddings model and a linear machine learning algorithm. This classifier serves as a baseline to compare to subsequent results. Second, we propose two ensemble techniques which aggregate our baseline classifier with other surface classifiers widely used in Sentiment Analysis. Third, we also propose two models for combining both surface and deep features to merge information from several sources. Fourth, we introduce a taxonomy for classifying the different models found in the literature, as well as the ones we propose. Fifth, we conduct several experiments to compare the performance of these models with the deep learning baseline. For this, we use seven public datasets that were extracted from the microblogging and movie reviews domain. Finally, as a result, a statistical study confirms that the performance of these proposed models surpasses that of our original baseline on F1-Score.

Enhancing deep learning sentiment analysis with ensemble techniques in social applications

Sentiment analysis is performed to extract opinion and subjectivity knowledge from user generated text content. This is contextually different from traditional topic based text classification since it involves classifying opinionated text according to the sentiment conveyed by it. Feature selection is a critical task in sentiment analysis and effectively selected representative features from subjective text can improve sentiment based classification. This paper explores the applicability of five commonly used feature selection methods in data mining research (DF, IG, GR, CHI and Relief-F) and seven machine learning based classification techniques (Naive Bayes, Support Vector Machine, Maximum Entropy, Decision Tree, K-Nearest Neighbor, Winnow, Adaboost) for sentiment analysis on online movie reviews dataset. The paper demonstrates that feature selection does improve the performance of sentiment based classification, but it depends on the method adopted and the number of feature selected. The experimental results presented in this paper show that Gain Ratio gives the best performance for sentimental feature selection, and SVM performs better than other techniques for sentiment based classification.

A comparative study of feature selection and machine learning techniques for sentiment analysis

https://research.ijcaonline.org/ACCTHPCA/number3/accthpca1020.pdf

Performance Investigation of Feature Selection Methods and Sentiment Lexicons for Sentiment Analysis

The abundance of discussion forums, Weblogs, e-commerce portals, social networking, product review sites and content sharing sites has facilitated flow of ideas and expression of opinions. The user-generated text content on Internet and Web 2.0 social media can be a rich source of sentiments, opinions, evaluations, and reviews. Sentiment analysis or opinion mining has become an open research domain that involves classifying text documents based on the opinion expressed, about a given topic, being positive or negative. This paper proposes a sentiment classification model using back-propagation artificial neural network (BPANN). Information Gain, and three popular sentiment lexicons are used to extract sentiment representing features that are then used to train and test the BPANN. This novel approach combines the strength of BPANN in classification accuracy with intrinsic subjectivity knowledge available in the sentiment lexicons. The results obtained from experiments on the movie and hotel review corpora have shown that the proposed approach has been able to reduce dimensionality, while producing accurate results for sentiment based classification of text.

A document-level sentiment analysis approach using artificial neural network and sentiment lexicons

The Internet and Web 2.0 social media have emerged as an important medium for expressing sentiments, opinions, evaluations, and reviews. Sentiment analysis or opinion mining is becoming an open research domain due to the abundance of discussion forums, Weblogs, e-commerce portals, social networking and content sharing sites where people tend to express their opinions. Sentiment Analysis involves classifying text documents based on the opinion expressed being positive or negative about a given topic. This paper proposes a sentiment classification model using back-propagation artificial neural network (BPANN). Information Gain and three popular sentiment lexicons are used to extract sentiment representing features that are then used to train and test the BPANN. This novel approach combines the strength of BPANN in classification accuracy with utilizing intrinsic domain knowledge available in the sentiment lexicons. The results obtained on the movie-review corpora have shown that the proposed approach has been able to reduce dimensionality, while producing accurate sentiment based classification of text.

An artificial neural network based approach for sentiment analysis of opinionated text

The purpose of this paper is to focus on failures in online flash sales (OFS) and to explore why consumers participate in an OFS even after experiencing service failure. It also examines the role of deal proneness, attribution, and emotions.,Using a mixed method approach to gain insights into this relatively unexplored phenomenon of OFS, this research uses netnography followed by a survey study.,The findings show that deal-prone customers tend to ignore service failures during OFS and re-participate in the future. In the context of OFS, failures attributed to internal locus of attribution (LOA) also have a negative effect on re-participation compared with failures attributed to external LOA. Furthermore, there is a three-way interaction among deal proneness, LOA, and past emotions. The results show that negative past emotions further exacerbate the impact of attribution on the link between deal proneness and re-participation.,In contrast with prior research, the paper shows that consumers participate even after service failure. The proposed difference is between customers who experience different LOA and past emotions offers insights into their behavior after service failure in a new context of an online/electronic commerce event – flash sales. This paper specifically explores the role of internal LOA and finds that it has a more negative impact than external LOA on re-participation.

Shubhamoy Dey

Papers

A comparative study of feature selection and machine learning techniques for sentiment analysis

Performance Investigation of Feature Selection Methods and Sentiment Lexicons for Sentiment Analysis

A document-level sentiment analysis approach using artificial neural network and sentiment lexicons

An artificial neural network based approach for sentiment analysis of opinionated text

Service failures after online flash sales: role of deal proneness, attribution, and emotion