Crowdsourced real-world sensing: sentiment analysis and the real-time web

The advent of the real-time web is proving both challeng- ing and at the same time disruptive for a number of areas of research, notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis and discusses the motivations and challenges behind such a direction

Good grief, i can speak it! Preliminary experiments in audio restaurant reviews

In this paper, we introduce a new envisioned application for speech which allows users to enter restaurant reviews orally via their mobile device, and, at a later time, update a shared and growing database of consumer-provided information about restaurants. During the intervening period, a speech recognition and NLP based system has analyzed their audio recording both to extract key descriptive phrases and to compute sentiment ratings based on the evidence provided in the audio clip. We report here on our preliminary work moving towards this goal. Our experiments demonstrate that multi-aspect sentiment ranking works surprisingly well on speech output, even in the presence of recognition errors. We also present initial experiments on integrated sentence boundary detection and key phrase extraction from recognition output

Sentiment and behaviour annotation in a corpus of dialogue summaries

This paper proposes a scheme for sentiment annotation. We show how the task can be made tractable by focusing on one of the many aspects of sentiment: sentiment as it is recorded in behaviour reports of people and their interactions. Together with a number of measures for supporting the reliable application of the scheme, this allows us to obtain sufficient to good agreement scores (in terms of Krippendorf's alpha) on three key dimensions: polarity, evaluated party and type of clause. Evaluation of the scheme is carried out through the annotation of an existing corpus of dialogue summaries (in English and Portuguese) by nine annotators. Our contribution to the field is twofold: (i) a reliable multi-dimensional annotation scheme for sentiment in behaviour reports; and (ii) an annotated corpus that was used for testing the reliability of the scheme and which is made available to the research community

The knowledge domain of affective computing: a scientometric review

Purpose – The aim of this study is to investigate the bibliographical information about Affective Computing identifying advances, trends, major papers, connections, and areas of research. Design/methodology/approach – A scientometric analysis was applied using CiteSpace, of 5,078 references about Affective Computing imported from the Web-of-Science Core Collection, covering the period of 1991-2016. Findings – The most cited, creative, burts and central references are displayed by areas of research, using metrics and througout-time visualization. Research limitations/implications – Interpretation is limited to references retrieved from theWeb-of-Science Core Collection in the fields of management, psychology and marketing. Nevertheless, the richness of bibliographical data obtained, largely compensates this limitation. Practical implications – The study provides managers with a sound body of knowledge on Affective Computing, with which they can capture general public emotion in respect of their products and services, and on which they can base their marketing intelligence gathering, and strategic planning. Originality/value – The paper provides new opportunities for companies to enhance their capabilities in terms of customer relationships.info:eu-repo/semantics/acceptedVersio

A review of opinion mining and sentiment classification framework in social networks

The Web has dramatically changed the way we express opinions on certain products that we have purchased and used, or for services that we have received in the various industries. Opinions and reviews can be easily posted on the Web. such as in merchant sites, review portals, blogs, Internet forums, and much more. These data are commonly referred to as usergenerated content or user-generated media. Both the product manufacturers, as well as potential customers are very interested in this online 'word-of-mouth', as it provides product manufacturers information on their customers likes and dislikes, as well as the positive and negative comments on their products whenever available, giving them better knowledge of their products limitations and advantages over competitors; and also providing potential customers with useful and 'first-hand' information on the products and/or services to aid in their purchase decision making process. This paper discusses the existing works on opinion mining and sentiment classification of customer feedback and reviews online, and evaluates the different techniques used for the process. It focuses on thc areas covered by the evaluated papers, points out the areas that are well covered by many researchers and areas that are neglected in opinion mining and sentiment classification which are open for future research opportunity

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

Sentiment Analysis Using Hybrid Machine Learning Technique

It is observed that consumers often share their opinion, views or feeling about any term used on social network in the form of reviews, comments or feedback. Those feedbacks given by end users have a great impact for evolution of new version of any product. Due to this trend in social media in recent years, sentiment analysis has become an important concern for theoreticians and practitioners Moreover reviews are often written in natural language and are mostly unstructured. Thus, to obtain any meaningful information from these reviews, it needs to be processed. Due to large size of data it is impossible to process this information manually. Hence machine learning algorithms are considered for analysis. Since data are unstructured in nature, unsupervised machine learning algorithm can be helpful in solving this problem. But unsupervised methods have less accuracy; hence not acceptable. In this study, a hybrid machine learning approach is adopted to automatically find the requirements for next version of software. Also some reviews neither belong to positive cluster nor to negative. They mixed reaction or feeling about some topics. Those problem associated with NLP is solved using hybrid technique of the fuzzy c-means and ANN. Moreover in this study, different methods of unsupervised machine leaning algorithm are implemented and their results are compared with each other. The best outcome is used to train the neural network. By using this hybridization technique, accuracy gets increased. And in later stage, this technique is applied to find the new requirement of product

Evaluation of Support Vector Machine and Decision Tree for Emotion Recognition of Malay Folklores

In this paper, the performance of Support Vector Machine (SVM) and Decision Tree (DT) in classifying emotions from Malay folklores is presented. This work is the continuation of our storytelling speech synthesis work to add emotions for a more natural storytelling. A total of 100 documents from children short stories are collected and used as the datasets of the text-based emotion recognition experiment. Term Frequency-Inverse Document Frequency (TF-IDF) is extracted from the text documents and classified using SVM and DT. Four types of common emotions, which are happy, angry, fearful and sad are classified using the two classifiers. Results showed that DT outperformed SVM by more than 22.2% accuracy rate. However, the overall emotion recognition is only at moderate rate suggesting an improvement is needed in future work. The accuracy of the emotion recognition should be improved in future studies by using semantic feature extractors or by incorporating deep learning for classification