Sentiment Analysis on UAV-aided Product Comments Based on Machine Learning: From Sentence to Document Level

2020 ◽  
Author(s):  
JINGYANG CAO ◽  
Shirong Yin ◽  
Guoxu Zhang
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Accurate Result ◽  
Supervised Machine Learning ◽  
Hotel Management ◽  
Novel Approach ◽  
Online Comments ◽  
New Perspective ◽  
The Relationship ◽  
Document Level

Abstract This paper presents a novel approach to analyze the sentiment of the product comments from sentence to document level and apply to the customers sentiment analysis on UAV-aided product comments for hotel management. In order to realize the effiffifficient sentiment analysis, a cascaded sentence-to-document sentiment classifification method is investigated. Initially, a supervised machine learning method is applied to explore the sentiment polarity of the sentence (SPS). Afterward, the contribution of the sentence to document (CSD) is calculated by using various statistical algorithms. Lastly, the sentiment polarity of the document (SPD) is determined by the SPS as well as its contribution. Comparative experiments have been established on the basis of hotel online comments, and the outcomes indicate that the proposed method not only raises the effiffifficiency in attaining a more accurate result but also assists immensely in regards to the B5G wireless communication supported by the UAV. The fifindings provide a new perspective that sentence position and its sentiment similarity with document (sentiment condition) dramatically disclose the relationship between sentence and document.

scholarly journals Sentiment Analysis Techniques and Application-Survey and Taxonomy

2021 ◽  
Vol 04 (01) ◽  
Author(s):  
Mahmood Umar ◽  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Sentiment Classification ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Generation ◽  
Analysis Techniques ◽  
Learning Techniques ◽  
Document Level

Nowadays, social media platforms, blogs, and e-commerce are commonly use to express opinion on politics, movies, products, education respectively; for election forecasting, business boosting and improvement of teaching and learning. As a result, data generation becomes easier; producing big data which requires appropriate techniques and tools to analyse easily, accurately and timely. Thus, making sentiment analysis very demanding research area. This study will investigate on what basis (sentiment classification level) or area of application (data source) do supervised machine learning approaches particularly Support Vector Machine (SVM), Naïve Bayes, and Maximum Entropy algorithms, and other technique-lexicon-based approach give the best result in sentiment analysis. Based on the review of the literature there is a contradiction on the point that SVM generated the best result in analyzing student sentiment on document level. This study also discovers that sentiment analysis differs from system to system based on polarity (types of the classes to predict: positive or negative, subjective or objective), different levels of classification (sentence, phrase, or document level) and language that is processed. This research produces a taxonomy which serves as a guide for the choice of techniques in sentiment analysis. The taxonomy explores the sentiment classification levels and data preprocessing stages. It also explores that sentiment analysis techniques were organised in to three (3) groups; Machine learning, Lexicon and hybrid or combination. The machine learning techniques were sub-grouped in to two (2) namely; supervised and unsupervised. The supervised were organized in to two (2): Classification and Regression. un-supervised machine learning techniques includes clustering and association. The clustering technique consist of k-means. Decision tree which is a classification based under supervised type of machine learning technique consist of random forest,(Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion. The commonly used tools are Weka, Python compiler, and R programming tool.

Machine Learning in Sentiment Analysis Over Twitter

2021 ◽  
pp. 126-144
Author(s):  
Kadda Zerrouki
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
K Nearest Neighbor ◽  
Aspect Extraction ◽  
Different Levels ◽  
Document Level ◽  
F Measure

Social networks are the main resources to gather information about people's opinions and sentiments towards different topics as they spend hours daily on social media and share their opinions. Twitter is a platform widely used by people to express their opinions and display sentiments on different occasions. Sentiment analysis's (SA) task is to label people's opinions as different categories such as positive and negative from a given piece of text. Another task is to decide whether a given text is subjective, expressing the writer's opinions, or objective. These tasks were performed at different levels of analysis ranging from the document level to the sentence and phrase level. Another task is aspect extraction, which originated from aspect-based sentiment analysis in phrase level. All these tasks are under the umbrella of SA. In recent years, a large number of methods, techniques, and enhancements have been proposed for the problem of SA in different tasks at different levels. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets) in order to extract sentiments conveyed by the user. In the past decades, the research in this field has consistently grown. The reason behind this is the challenging format of the tweets, which makes the processing difficult. The tweet format is very small, which generates a whole new dimension of problems like use of slang, abbreviations, etc. The chapter elaborately discusses three supervised machine learning algorithms—naïve Bayes, k-nearest neighbor (KNN), and decision tree—and compares their overall accuracy, precisions, as well as recall values; f-measure; number of tweets correctly classified; number of tweets incorrectly classified; and execution time.

scholarly journals Financial Context News Sentiment Analysis for the Lithuanian Language

2021 ◽  
Vol 11 (10) ◽  
pp. 4443
Author(s):  
Rokas Štrimaitis ◽  
Pavel Stefanovič ◽  
Simona Ramanauskaitė ◽  
Asta Slotkienė
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Experimental Investigations ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Bayes Algorithm ◽  
Website Content

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

Multilingual Sentiment Analysis on Short Text Document Using Semi-Supervised Machine Learning

2021 ◽  
Author(s):  
Joshua Lois Cruz Paulino ◽  
Lexter Carl Antoja Almirol ◽  
Jun Marco Cruz Favila ◽  
Kent Alvin Gerald Loria Aquino ◽  
Angelica Hernandez De La Cruz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Supervised Machine Learning ◽  
Short Text ◽  
Text Document

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary

2021 ◽  
Vol 1 (3-4) ◽  
pp. 34
Author(s):  
Nitish Ranjan Bhowmik ◽  
Mohammad Arifuzzaman ◽  
M. Rubaiyat Hossain Mondal ◽  
M. S. Islam
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Supervised Machine Learning ◽  
Text Sentiment Analysis

Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

2021 ◽  
Author(s):  
Dimple Chehal ◽  
Parul Gupta ◽  
Payal Gulati
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Product Reviews ◽  
K Nearest Neighbor ◽  
Customer Reviews ◽  
Percent Accuracy

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

A Novel Algorithm for Sentiment Analysis of Online Movie Reviews

2018 ◽  
pp. 106-140
Author(s):  
Bisma Shah ◽  
Farheen Siddiqui
Keyword(s):  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
World Knowledge ◽  
K Nearest Neighbor ◽  
Customer Feedback ◽  
Novel Approach ◽  
The One ◽  
Novel Algorithm

Others' opinions can be decisive while choosing among various options, especially when those choices involve worthy resources like spending time and money buying products or services. Customers relying on their peers' past reviews on e-commerce websites or social media have drawn a considerable interest to sentiment analysis due to realization of its commercial and business benefits. Sentiment analysis can be exercised on movie reviews, blogs, customer feedback, etc. This chapter presents a novel approach to perform sentiment analysis of movie reviews given by users on different websites. Also, challenges like presence of thwarted words, world knowledge, and subjectivity detection in sentiments are addressed in this chapter. The results are validated by using two supervised machine learning approaches, k-nearest neighbor and naive Bayes, both on method of sentiment analysis without addressing aforementioned challenges and on proposed method of sentiment analysis with all challenges addressed. Empirical results show that proposed method outperformed the one that left challenges unaddressed.

Sign in / Sign up

Export Citation Format

Share Document