Social information discovery enhanced by sentiment analysis techniques

Diamantini, Claudia; Mircoli, Alex; Potena, Domenico; Storti, Emanuele

doi:10.1016/j.future.2018.01.051

Cited by 45 publications

(25 citation statements)

References 27 publications

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“…We compared our opinion words polarity from HEOLS with the opinion word polarity from: 1) Opinion Lexicon; 2) the first sense of adjective word SentiWordNet [30] (positive if the SentiWordNet score > 0 and vice versa), we use SentiWordNet because it was used in previous research [31,32]; and 3) same as in point 2 but we add Word Sense Disambiguation (WSD) using Adapted Lesk [33] to improve the performance [34][35][36].…”

Section: Comparisonmentioning

confidence: 99%

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Nurifan¹,

Sarno²,

Sungkono³

2019

IJIES

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Many restaurant review analysis have been done, however only few analysis have been done for specific aspects of a restaurant. In this context this paper proposes aspect based restaurant analysis which includes Physical environment, Food quality, Service quality and Price fairness. The analysis steps include Aspect Term Extraction (ATE), Aspect Keyword Extraction (AKE), Aspect Categorization (AC) and Sentiment Analysis (SA). ATE employs the modification of Double Propagation method and several Topic Modelling methods, AKE utilizes Term Frequency-Inverse Cluster Frequency (TF-ICF), in AC we propose Hybrid ELMo-Wikipedia (HEW), and in SA we propose Hybrid Expanded Opinion Lexicon-SentiCircle (HEOLS). The results show that our modification of the methods used in ATE could increase the f1measure of the AC by average 2%, then the HEW that we proposed had better f1measure compared to other similar methods by average 6%. Other than that, our proposed HEOLS can expand and redetermine the Opinion Lexicon polarity and can increase f1measure of SA by 6%.

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Section: Comparisonmentioning

confidence: 99%

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Nurifan¹,

Sarno²,

Sungkono³

2019

IJIES

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“…Sentiment analysis has recently become a popular research field. ere are many studies to specify and classify users' feelings by using social media platforms [42,43]. It is usually difficult to analyze texts for interpreting emotions.…”

Section: Related Workmentioning

confidence: 99%

Mood Detection from Physical and Neurophysical Data Using Deep Learning Models

Kilimci

Guven²,

Uysal

et al. 2019

Complexity

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Nowadays, smart devices as a part of daily life collect data about their users with the help of sensors placed on them. Sensor data are usually physical data but mobile applications collect more than physical data like device usage habits and personal interests. Collected data are usually classified as personal, but they contain valuable information about their users when it is analyzed and interpreted. One of the main purposes of personal data analysis is to make predictions about users. Collected data can be divided into two major categories: physical and behavioral data. Behavioral data are also named as neurophysical data. Physical and neurophysical parameters are collected as a part of this study. Physical data contains measurements of the users like heartbeats, sleep quality, energy, movement/mobility parameters. Neurophysical data contain keystroke patterns like typing speed and typing errors. Users’ emotional/mood statuses are also investigated by asking daily questions. Six questions are asked to the users daily in order to determine the mood of them. These questions are emotion-attached questions, and depending on the answers, users’ emotional states are graded. Our aim is to show that there is a connection between users’ physical/neurophysical parameters and mood/emotional conditions. To prove our hypothesis, we collect and measure physical and neurophysical parameters of 15 users for 1 year. The novelty of this work to the literature is the usage of both combinations of physical and neurophysical parameters. Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. Multinomial Naïve Bayes (MNB), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Decision Integration Strategy (DIS) are evaluated as conventional machine learning algorithms. To the best of our knowledge, this is the very first attempt to analyze the neurophysical conditions of the users by evaluating deep learning models for mood analysis and enriching physical characteristics with neurophysical parameters. Experiment results demonstrate that the utilization of deep learning methodologies and the combination of both physical and neurophysical parameters enhances the classification success of the system to interpret the mood of the users. A wide range of comparative and extensive experiments shows that the proposed model exhibits noteworthy results compared to the state-of-art studies.

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“…Garcia et al [16] present probabilistic classifier to highlight the negativity, Korkontzelos et al [17] use part of speech (POS) to evaluate grammatical dependency among negation cue and opinionated word in medical area. Diamantini et al [18] use depth-first search (DFS) strategy for building grammatical dependency tree to identify of negation cues. Tian Kang et al [19] use Conditional Random Fields (CRF) for 'BIO' tagging to represent the boundaries of negation cues.…”

Section: Related Workmentioning

confidence: 99%

“…(c) Grammatical Dependency Tree: -Grammatical dependency between orders of occurrence of sentiments oriented word and negative cue help to figure out influence of NEGATION token [18]. Grammatical dependency parser build syntactic tree [28] and their lowest level are help to figure out scope of negation.…”

Section: Negation Feature Extractionmentioning

confidence: 99%

Feature Fusion for Negation Scope Detection in Sentiment Analysis: Comprehensive Analysis over Social Media

Singh¹,

Singh²

2019

IJACSA

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Negation control for sentiment analysis is essential and effective decision support system. Negation control include identification of negation cues, scope of negation and their influence within it. Negation can either shift or change the polarity score of opinionated word. This paper present a framework for feature fusion of text feature extraction, negation cue and scope detection technique for enhancing the performance of recent sentiment classifier for negation control. Explore text feature POS, BOW and HT with negation cue and scope detection techniques for classification technique over social media data set. This paper has included the evaluation of sentiment classification (Support vector machine, Navies Bayes, Linear Regression and Random Forest) and Nine feature fusion over presented prepossessing framework. This paper yield interesting result about collective response of feature fusion for negation scope detection and classification technique. Feature Fusion vector significantly increase the polarity classification accuracy of sentiment classification technique. POS with Grammatical dependency tree can detect negation with better accuracy as compared to other feature fusion.

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Social information discovery enhanced by sentiment analysis techniques

Cited by 45 publications

References 27 publications

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Mood Detection from Physical and Neurophysical Data Using Deep Learning Models

Feature Fusion for Negation Scope Detection in Sentiment Analysis: Comprehensive Analysis over Social Media

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