scholarly journals Aspect-Based Sentiment Analysis Using Smart Company and Hotel Aspect Review Data

2020 ◽  
pp. 112-118
Author(s):  
C. Selvi ◽  
Niveda. C. P
Keyword(s):  
Knowledge Acquisition ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Real World ◽  
Performance Improvements ◽  
Information Navigation ◽  
Real World Applications ◽  
Significant Performance ◽  
Processing Techniques ◽  
Online Feedback

Digital sources such as smart applications opinions and online feedback statistics are crucial resources to be seeking for customers’ remarks and input. However, the reviews are often disorganized, leading to difficulties in information navigation and knowledge acquisition. The aforementioned problem is overcome by generating aspect-sentiment based embedding for the hotels and companies by looking into reliable reviews of them. The important product aspects are identified based on two observations: 1) the important aspects are usually commented on by a large number of consumers and 2) consumer opinions on the important aspects greatly influence their overall opinions. Aspect frequency and the influence of consumer opinions given to each aspect over their overall opinions are identified for hotel reviews whereas for company reviews approach adopts language processing techniques, policies, and lexicons to address several sentiment evaluation challenges, and convey summarized results. Moreover, aspect ranking achieve significant performance improvements, which demonstrate the capacity of aspect ranking in facilitating real-world applications.

scholarly journals Aspect-based sentiment analysis using smart government review data

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Omar Alqaryouti ◽  
Nur Siyam ◽  
Azza Abdel Monem ◽  
Khaled Shaalan
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Hybrid Approach ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approaches ◽  
Feedback Information ◽  
Aspect Extraction ◽  
Processing Techniques ◽  
Online Feedback

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

Parsing

2012 ◽  
Author(s):  
John Carroll
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Level Of Detail ◽  
Semantic Interpretation ◽  
Syntactic Analysis ◽  
Real World Applications ◽  
Grammar Formalisms ◽  
Speech Recognizer ◽  
Context Free

This article introduces the concepts and techniques for natural language (NL) parsing, which signifies, using a grammar to assign a syntactic analysis to a string of words, a lattice of word hypotheses output by a speech recognizer or similar. The level of detail required depends on the language processing task being performed and the particular approach to the task that is being pursued. This article further describes approaches that produce ‘shallow’ analyses. It also outlines approaches to parsing that analyse the input in terms of labelled dependencies between words. Producing hierarchical phrase structure requires grammars that have at least context-free (CF) power. CF algorithms that are widely used in parsing of NL are described in this article. To support detailed semantic interpretation more powerful grammar formalisms are required, but these are usually parsed using extensions of CF parsing algorithms. Furthermore, this article describes unification-based parsing. Finally, it discusses three important issues that have to be tackled in real-world applications of parsing: evaluation of parser accuracy, parser efficiency, and measurement of grammar/parser coverage.

scholarly journals An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

2020 ◽  
Author(s):  
Sohini Sengupta ◽  
Sareeta Mugde ◽  
Garima Sharma
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Gold Mine ◽  
Social Media Platforms ◽  
Processing Techniques

Twitter is one of the world's biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users.

scholarly journals Let’s play on Facebook: using sentiment analysis and social media metrics to measure the success of YouTube gamers’ post types

2019 ◽  
Author(s):  
Flora Poecze ◽  
Claus Ebster ◽  
Christine Strauss
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Nearest Neighbor ◽  
Consumer Feedback ◽  
Youtube Videos ◽  
The Masses ◽  
Processing Techniques ◽  
Facebook Pages ◽  
Future Work

AbstractThis paper discusses the analysis results of successful self-marketing techniques on Facebook pages in the cases of three YouTube gamers: PewDiePie, Markiplier, and Kwebbelkop. The research focus was to identify significant differences in terms of the gamers’ user-generated Facebook metrics and commentary sentiments. Analysis of variance (ANOVA) and k-nearest neighbor sentiment analysis were employed as core research methods. ANOVA of the classified post categories revealed that photos tended to show significantly more user-generated interactions than other post types, while, on the other hand, re-posted YouTube videos gained significantly fewer numbers in the retrieved metrics than other content types. K-nearest neighbor sentiment analysis pointed out underlying follower negativity in cases where user-generated activity was relatively low, thereby improving the understanding of the opinion of the masses previously hidden behind metrics such as the number of likes, comments, and shares. The paper at hand highlights the methodological design of the study as well as a detailed discussion of key findings and their implications, and future work. The results per se indicate the need to utilize natural language processing techniques to optimize brand communication on social media and highlight the importance of considering machine learning sentiment analysis techniques for a better understanding of consumer feedback.

scholarly journals Optimasi Textblob Menggunakan Support Vector Machine Untuk Analisis Sentimen (Studi Kasus Layanan Telkomsel)

2021 ◽  
Vol 10 (1) ◽  
pp. 13-15
Author(s):  
Kevin Perdana ◽  
Titania Pricillia ◽  
Zulfachmi
Keyword(s):  
Support Vector Machine ◽  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
A Value ◽  
Processing Techniques

Sentiment analysis refers to Natural Language Processing techniques that are classified as Unsupervised Learning to identify positive, negative, or neutral opinions. Many of these opinions come through Twitter, because social media is quite effective and efficient in commenting because it can only write a maximum of 140 characters. From previous research, the value of the accuracy of the sentiment analysis carried out by one of the NLP libraries, namely TextBlob, has shown that Unsupervised Learning does not produce such good scores. With the Telkomsel service case study the writer took the dataset from Twitter and the results of the analysis with TextBlob only showed a value of 58.59%. Optimization is done by adding the Support Vector Machine method which is included in the Supervised Learning category. The best results obtained from this study are values that show 75%.

Offline vs. Online Sentiment Analysis

2017 ◽  
Vol 7 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Ritesh Srivastava ◽  
M.P.S. Bhatia
Keyword(s):  
Sentiment Analysis ◽  
Real Time ◽  
Language Processing ◽  
Real World ◽  
Social Networking Sites ◽  
The Real ◽  
Intelligent Decision Making ◽  
Real Time Analysis ◽  
Analysis Process ◽  
Intelligent Decision

Recently, the social networking sites (SNSs) have proven their immense power of prediction for predicting the results of the real-world events. However, for real-time monitoring of the world activities via microblogging site like Twitter, it is important to perform the sentiment analysis of online micro-texts in real-time to support fast and intelligent decision-making and hence to execute the appropriate actions in the real world in real-time. In this context, this paper discusses the online sentiment analysis process of online micro-texts in perspectives of the real-time analysis process. In addition, this paper argues the non-applicability of the classical time consuming Natural Language Processing (NLP) methods and the affinity of Machine Learning (ML) methods in performing the online sentiment analysis by contrasting it with offline sentiment analysis. Furthermore, it also formalized the online sentiment analysis process of online micro-texts by raising novel issues and proposing new performance measures for online sentiment analysis.

scholarly journals AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

2021 ◽  
Author(s):  
Niloufar Shoeibi ◽  
Nastaran Shoeibi ◽  
Guillermo Hernández ◽  
Pablo Chamoso ◽  
Juan Manuel Corchado
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Hate Speech ◽  
Freedom Of Expression ◽  
Classification Model ◽  
Abnormal Behavior ◽  
Proper Actions ◽  
Abnormal Behavior Detection ◽  
Metadata Analysis ◽  
Processing Techniques

Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

scholarly journals Usense: A User Sentiment Analysis Model for Movie Reviews by Applying LSTM

2020 ◽  
pp. 369-372
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Linear Kernel ◽  
Analysis Model ◽  
Large Dataset ◽  
Major Drawback ◽  
User Comments ◽  
Processing Techniques ◽  
Better Than

Sentiment analysis is a field which deals with assessing the sentiments or emotions of the users on products and services. It takes user comments as input and applies natural language processing techniques to identify the mood of the user. Usually a sentiment is deemed to be positive, negative or neutral depending upon the mood that he expresses in the comments or feedbacks. It is largely used by businesses to improve products and services and also to present its customers with a set of products and services based on their likes and dislikes. State-of-the-art indicates many techniques have been applied in past such as, linear regression and SVM models. Recurrent Neural Networks (RNNs) have improved the way in which sentiment analysis could be done with greater accuracy, but they suffer from major drawback when applied to longer sentences. This paper proposes a sentiment analysis model using Long ShortTerm Memory (LSTM) based approach , which is a variant of RNNs. LSTMs are good in handling long sentence data. The model is applied to reviews collected from IMDB dataset. It is large dataset that contains 50K reviews. Out of the available reviews 50 % are used for training purpose and 50% are used for testing purpose. The model gives a training accuracy of 92% and validation accuracy of 85% which is neither an over fit nor an under fit. The overall accuracy here is 85%, which seems to be better than some of the existing techniques such as SVM with linear kernel.

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