Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes

The authors address two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior accuracy in converting text to numerical attribute sentiment scores with their model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings

Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes

The authors address two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior accuracy in converting text to numerical attribute sentiment scores with their model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings

Interdisciplinary Approach to Emotion Detection from Text

Emotions not only influence most aspects of cognition and behavior, but also play a prominent role in interaction and communication between people. With current multidimensional research on emotions being vast and varied, all researchers of emotions, both psychologists and linguists alike, agree that emotions are at the core of understanding ourselves and others. As a primary vehicle of communication and interaction, language is the most convenient medium for approaching research on the topic of emotions. Not only is one of the main functions of language the emotive one, but the interplay of emotions and language occurs at all linguistic levels. Textual data, in particular, can be beneficial to emotion detection due to its syntactic and semantic information containing not only informative content, but emotional states as well. A general overview of the emotion models based on the research in psychology, as well as the major approaches to emotion detection from text found in linguistics, together with usage demonstration of emotion detection linguistic tools, will be given in this paper. Examples of useful applications – from psychologists analyzing session transcripts in search for any subtle emotions, over public opinion mining on social networks to the development of AI technology – will also be provided showing that emotion detection from text has an abundance of practical uses. As the methods for emotion detection from text become more accurate, uses and applications of emotion detection from text will become more numerous and diverse in the future

Domain-specific lexicon generation for emotion detection from text.

Emotions play a key role in effective and successful human communication. Text is popularly used on the internet and social media websites to express and share emotions, feelings and sentiments. However useful applications and services built to understand emotions from text are limited in effectiveness due to reliance on general purpose emotion lexicons that have static vocabulary and sentiment lexicons that can only interpret emotions coarsely. Thus emotion detection from text calls for methods and knowledge resources that can deal with challenges such as dynamic and informal vocabulary, domain-level variations in emotional expressions and other linguistic nuances. In this thesis we demonstrate how labelled (e.g. blogs, news headlines) and weakly-labelled (e.g. tweets) emotional documents can be harnessed to learn word-emotion lexicons that can account for dynamic and domain-specific emotional vocabulary. We model the characteristics of realworld emotional documents to propose a generative mixture model, which iteratively estimates the language models that best describe the emotional documents using expectation maximization (EM). The proposed mixture model has the ability to model both emotionally charged words and emotion-neutral words. We then generate a word-emotion lexicon using the mixture model to quantify word-emotion associations in the form of a probability vectors. Secondly we introduce novel feature extraction methods to utilize the emotion rich knowledge being captured by our word-emotion lexicon. The extracted features are used to classify text into emotion classes using machine learning. Further we also propose hybrid text representations for emotion classification that use the knowledge of lexicon based features in conjunction with other representations such as n-grams, part-of-speech and sentiment information. Thirdly we propose two different methods which jointly use an emotion-labelled corpus of tweets and emotion-sentiment mapping proposed in psychology to learn word-level numerical quantification of sentiment strengths over a positive to negative spectrum. Finally we evaluate all the proposed methods in this thesis through a variety of emotion detection and sentiment analysis tasks on benchmark data sets covering domains from blogs to news articles to tweets and incident reports

A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets

With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM)

EVALITA Evaluation of NLP and Speech Tools for Italian Proceedings of the Final Workshop

Editor of the proceedings of EVALITA 2016

D1.3 List of available solutions

This report has been submitted by Tempesta Media SL as deliverable D1.3 within the framework of H2020 project "SO-CLOSE: Enhancing Social Cohesion through Sharing the Cultural Heritage of Forced Migrations" Grant No. 870939.This report aims to conduct research on the specific topics and needs of the SO-CLOSE project, addressing the available solutions through a state-of-the-art digital tools analysis, applied in the cultural heritage and migration fields. More specifically the report's scope is:To define proper tools and proceedings for the interview needs -performing, recording, transcription, translation. To analyse potential content gathering tools for the co-creation workshops. To conduct a state-of-the-art sharing tools analysis, applied in the cultural heritage and migration fields, and propose a critically adjusted and innovative digital approach

Emotional Tendency Analysis of Twitter Data Streams

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi