With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty-one articles.

A survey on opinion mining and sentiment analysis

In this paper, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about. We used a 7-layer deep convolutional neural network to tag each word in opinionated sentences as either aspect or non-aspect word. We also developed a set of linguistic patterns for the same purpose and combined them with the neural network. The resulting ensemble classifier, coupled with a word-embedding model for sentiment analysis, allowed our approach to obtain significantly better accuracy than state-of-the-art methods.

Aspect extraction for opinion mining with a deep convolutional neural network

A divide-and-conquer method classifying sentence types before sentiment analysis.Classifying sentence types by the number of opinion targets a sentence contain.A data-driven approach automatically extract features from input sentences. Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.

https://cyberleninka.org/article/n/1446987.pdf

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human–machine interaction using spoken language. Spoken dialogue technology allows various interactive applications to be built and used for practical purposes, and research focuses on issues that aim to increase the system’s communicative competence by including aspects of error correction, cooperation, multimodality, and adaptation in context. This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems. It provides an overview of the basic issues such as system architectures, various dialogue management methods, system evaluation, and also surveys advanced topics concerning extensions of the basic model to more conversational setups. The goal of the book is to provide an introduction to the methods, problems, and solutions that are used in dialogue system development and evaluation. It presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research. vi KEywoRDS Spoken dialogue systems, multimodality, evaluation, error-handling, dialogue management, statistical method v MC_Jok nen_FM. ndd Achorn Internat onal 10/10/2009 04:18AM

https://www.morganclaypool.com/doi/suppl/10.2200/S00509ED1V01Y201305HLT023/suppl_file/Dagan_Ch1.pdf

Synthesis Lectures on Human Language Technologies

Distinguishing between facts and opinions for sentiment analysis: survey and challenges

In this work, we present a system called PoliTwi, which was designed to detect emerging political topics (Top Topics) in Twitter sooner than other standard information channels. The recognized Top Topics are shared via different channels with the wider public. For the analysis, we have collected about 4,000,000 tweets before and during the parliamentary election 2013 in Germany, from April until September 2013. It is shown, that new topics appearing in Twitter can be detected right after their occurrence. Moreover, we have compared our results to Google Trends. We observed that the topics emerged earlier in Twitter than in Google Trends.

Finally, we show how these topics can be used to extend existing knowledge bases (web ontologies or semantic networks) which are required for concept-level sentiment analysis. For this, we utilized special Twitter hashtags, called sentiment hashtags, used by the German community during the parliamentary election.

PoliTwi: early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis

In this paper we present an approach to generate lists of opinion bearing phrases with their opinion values in a continuous range between -- 1 and 1. Opinion phrases that are considered include single adjectives as well as adjective-based phrases with an arbitrary number of words. The opinion values are derived from user review titles and star ratings, as both can be regarded as summaries of the user's opinion about the product under review. Phrases are organized in trees with the opinion bearing adjective as tree root. For trees with missing branches, opinion values then can be calculated using trees with similar branches but different roots. An example list is produced and compared to existing opinion lists.

A generic approach to generate opinion lists of phrases for opinion mining applications

Many factors trigger migraine attacks. Weather is often reported to be one of the most common migraine triggers. However, there is little scientific evidence about the underlying mechanisms and causes. In our pilot study, we used smartphone apps and a web form to collect around 4,700 migraine messages in Germany between June 2011 and February 2012. Taking interdiurnal temperature changes as an indicator for changes in the prevailing meteorological conditions, our analyses were focused on the relationship between temperature changes and the frequency of occurrence of migraine attacks. Linear trends were fitted to the total number of migraine messages with respect to temperature changes. Statistical and systematic errors were estimated. Both increases and decreases in temperature lead to a significant increase in the number of migraine messages. A temperature increase (decrease) of 5 °C resulted in an increase of 19 ± 7 % (24 ± 8 %) in the number of migraine messages.

Influence of temperature changes on migraine occurrence in Germany.

In this paper, we present a study of aspect-based opinion mining using a lexicon-based approach. We use a phrase-based opinion lexicon for the German language to investigate, how good strong positive and strong negative expressions of opinions, concerning products and services in the insurance domain, can be detected. We perform experiments on hand-tagged statements expressing opinions retrieved from the Ciao platform. The initial corpus contained about 14,000 sentences from 1,600 reviews. For both, positive and negative statements, more than 100 sentences were tagged. We show, that the algorithm can reach an accuracy of 62.2% for positive, but only 14.8% for negative utterances of opinions. We examine the cases, in which the opinion could not correctly be detected or in which the linking between the opinion statement and the aspect fails. Especially, the large gap in accuracy between positive and negative utterances is analysed.

Evaluation of an algorithm for aspect-based opinion mining using a lexicon-based approach

OBJECTIVES The purpose of this work is the analysis of migraine attack reports collected online within the project Migraine Radar in respect to the distribution of the migraine attacks over the week on a single-participant level. MATERIALS & METHODS Recording data using a web app as well as smartphone apps made it possible to collect data of 44 639 migraine attacks of 1085 participants who reported seven or more attacks over a participation period of at least 90 days. This allows the investigation of attack distributions on a single-participant level. Considering the day of the week with the highest attack frequency for each participant-the mode of the individual distribution-allows identifying participants suffering from weekend migraines. Namely, a weekend pattern is assumed if the mode falls on a Saturday or Sunday. RESULTS For 15.9% of the participants, the attacks were not distributed equally (P < 0.05) over the days of the week. Instead, participants show different individual patterns for the distribution of their migraine attacks. Furthermore, the modes of the individual distributions are not distributed equally over the week. In fact, Saturday seems to be the predominant day for migraine attacks for a greater proportion of participants (195 of 1085). CONCLUSIONS Concerning the individual attack distributions, we found that participants show individual attack patterns and weekend migraine can be determined for a subgroup of participants, while other participants show accumulations of their attacks on other days of the week.

Dirk Reinel

Papers

PoliTwi: early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis

A generic approach to generate opinion lists of phrases for opinion mining applications

Influence of temperature changes on migraine occurrence in Germany.

Evaluation of an algorithm for aspect-based opinion mining using a lexicon-based approach

Distribution of migraine attacks over the days of the week: Preliminary results from a web-based questionnaire.