Sentiment Analysis on Twitter Hashtag Datasets

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%).

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Feature-Based Opinion Mining and Managed Machine Learning with Sentiment Classification Models

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4555.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3992-3998

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Opinion Mining ◽

Machine Learning Algorithms ◽

Support Vector ◽

K Nearest Neighbors ◽

Data Intensive ◽

Learning Tasks ◽

Feature Based

Sentiment Analysis is individuals' opinions and feedbacks study towards a substance, which can be items, services, movies, people or events. The opinions are mostly expressed as remarks or reviews. With the social network, gatherings and websites, these reviews rose as a significant factor for the client’s decision to buy anything or not. These days, a vast scalable computing environment provides us with very sophisticated way of carrying out various data-intensive natural language processing (NLP) and machine-learning tasks to examine these reviews. One such example is text classification, a compelling method for predicting the clients' sentiment. In this paper, we attempt to center our work of sentiment analysis on movie review database. We look at the sentiment expression to order the extremity of the movie reviews on a size of 0(highly disliked) to 4(highly preferred) and perform feature extraction and ranking and utilize these features to prepare our multilabel classifier to group the movie review into its right rating. This paper incorporates sentiment analysis utilizing feature-based opinion mining and managed machine learning. The principle center is to decide the extremity of reviews utilizing nouns, verbs, and adjectives as opinion words. In addition, a comparative study on different classification approaches has been performed to determine the most appropriate classifier to suit our concern problem space. In our study, we utilized six distinctive machine learning algorithms – Naïve Bayes, Logistic Regression, SVM (Support Vector Machine), RF (Random Forest) KNN (K nearest neighbors) and SoftMax Regression.

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Aspect Based Sentimental Analysis of Hotel Reviews: A Comparative Study

Sukkur IBA Journal of Computing and Mathematical Sciences ◽

10.30537/sjcms.v4i1.567 ◽

2020 ◽

Vol 4 (1) ◽

pp. 11-20

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Sentiment Analysis ◽

Opinion Mining ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Practical Significance ◽

Support Vector ◽

Use Of The Internet ◽

And Task

The increasing use of the internet enables users to share their opinion about what they like and dislike regarding products and services. For efficient decision making, there is a need to analyze these reviews. Sentiment analysis or opinion mining is commonly used to detect polarity (positive or negative) of reviews. But, it does not show the aspect or orientation of the text. In this study, state-of-art approaches based on supervised machine learning employed to perform three tasks on the dataset provided by SemEval. Tasks A and B are related to predicting the aspect of the restaurant’s reviews, whereas task C shows their polarity. Additionally, this study aims to compare the performance of two feature engineering techniques and five machine learning algorithms to evaluate their performance on a publicly available dataset named SemEval-2015 Task 12. The experimental results showed that the word2vec features when used with the support vector machine algorithm outperformed by giving 76%, 72% and 79% off overall accuracies for Task A, Task B, and Task C respectively. Our comparative study holds practical significance and can be used as a baseline study in the domain of aspect-based sentiment analysis.

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Sentiment Analysis on Social Media using Machine Learning Approach

10.22541/au.163620143.37655829/v1 ◽

2021 ◽

Author(s):

Erick Omuya ◽

George Okeyo ◽

Michael Kimwele

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Language Processing ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approach ◽

K Nearest Neighbor ◽

Machine Learning Approach

Social media has been embraced by different people as a convenient and official medium of communication. People write messages and attach images and videos on Twitter, Facebook and other social media which they share. Social media therefore generates a lot of data that is rich in sentiments from these updates. Sentiment analysis has been used to determine opinions of clients, for instance, relating to a particular product or company. Knowledge based approach and Machine learning approach are among the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is however distorted by noise, the curse of dimensionality, the data domains and size of data used for training and testing. This research aims at developing a model for sentiment analysis in which dimensionality reduction and the use of different parts of speech improves sentiment analysis performance. It uses natural language processing for filtering, storing and performing sentiment analysis on the data from social media. The model is tested using Naïve Bayes, Support Vector Machines and K-Nearest neighbor machine learning algorithms and its performance compared with that of two other Sentiment Analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.

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Sentiment Analysis of Customer Product Reviews using deep Learning and Compare with other Machine Learning Techniques

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36202 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 233-239

Author(s):

Amit Purohit

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Opinion Mining ◽

Machine Learning Techniques ◽

Support Vector ◽

Product Reviews ◽

Product Analysis ◽

Learning Techniques ◽

Machine Learning Approach

Sentiment analysis is defined as the process of mining of data, view, review or sentence to Predict the emotion of the sentence through natural language processing (NLP) or Machine Learning Techniques. The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. The process of finding user Opinion about the topic or Product or problem is called as opinion mining. Analyzing the emotions from the extracted Opinions are defined as Sentiment Analysis. The goal of opinion mining and Sentiment Analysis is to make computer able to recognize and express emotion. Using social media, E-commerce website, movies reviews such as Face book, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. Sentiment analysis in a machine learning approach in which machines classify and analyze the human’s sentiments, emotions, opinions etc. about the products. Out of the various classification models, Naïve Bayes, Support Vector Machine (SVM) and Decision Tree are used maximum times for the product analysis. The proposed approach will do better result as compare to other machine learning techniques.

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Machine Learning Approaches for Sentiment Analysis

Data Mining and Analysis in the Engineering Field - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-6086-1.ch011 ◽

2014 ◽

pp. 193-208 ◽

Cited By ~ 9

Author(s):

Basant Agarwal ◽

Namita Mittal

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Opinion Mining ◽

Machine Learning Algorithms ◽

Sentiment Classification ◽

Learning Approaches ◽

Learning Methods ◽

Machine Learning Methods ◽

Knowledge Based ◽

Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

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Opinion Mining

Advances in Data Mining and Database Management - Extracting Knowledge From Opinion Mining ◽

10.4018/978-1-5225-6117-0.ch004 ◽

2019 ◽

pp. 66-82 ◽

Cited By ~ 8

Author(s):

Vijender Kumar Solanki ◽

Nguyen Ha Huy Cuong ◽

Zonghyu (Joan) Lu

Keyword(s):

Machine Learning ◽

Information Technology ◽

Sentiment Analysis ◽

Programming Languages ◽

Opinion Mining ◽

Support Vector ◽

Emerging Trends ◽

The One ◽

The Mind ◽

Technology Domain

The machine learning is the emerging research domain, from which number of emerging trends are available, among them opinion mining is the one technology attraction through which the we could get analysis of the interested domain or we can say about the review from the customer towards any product or we can say any upcoming trending information. These two are the emerging words and we can say it's the buzz word in the information technology. As you will see that its widely use by the corporate sector to uplift the business next level. Before two decade you will not read any words e.g., Opinion mining or Sentiment analysis, but in the last two decade these words have given a new life to information technology domain as well as to the business. The important question which runs in the mind is why use sentiment analysis or opinion mining. The information technology has given number of new programming languages, new innovation and within that the data mining has given this trends to the users. The chapter is covering the three major concept's which comes under the machine learning e.g., Decision tree, Bayesian network and Support vector machine. The chapter is describing the basic inputs, and how it helps in supporting stakeholders by adopting these technologies.

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Machine Learning Approaches for Sentiment Analysis

Big Data ◽

10.4018/978-1-4666-9840-6.ch088 ◽

2016 ◽

pp. 1917-1933

Author(s):

Basant Agarwal ◽

Namita Mittal

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Opinion Mining ◽

Machine Learning Algorithms ◽

Sentiment Classification ◽

Learning Approaches ◽

Learning Methods ◽

Machine Learning Methods ◽

Knowledge Based ◽

Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

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Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis

International Journal of Knowledge Discovery in Bioinformatics ◽

10.4018/ijkdb.2017010103 ◽

2017 ◽

Vol 7 (1) ◽

pp. 30-41 ◽

Cited By ~ 12

Author(s):

Prayag Tiwari ◽

Brojo Kishore Mishra ◽

Sachin Kumar ◽

Vivek Kumar

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Maximum Entropy ◽

Learning Strategies ◽

Supervised Machine Learning ◽

Support Vector ◽

N Gram ◽

F Measure ◽

Blog Posts

Sentiment Analysis intends to get the basic perspective of the content, which may be anything that holds a subjective supposition, for example, an online audit, Comments on Blog posts, film rating and so forth. These surveys and websites might be characterized into various extremity gatherings, for example, negative, positive, and unbiased keeping in mind the end goal to concentrate data from the info dataset. Supervised machine learning strategies group these reviews. In this paper, three distinctive machine learning calculations, for example, Support Vector Machine (SVM), Maximum Entropy (ME) and Naive Bayes (NB), have been considered for the arrangement of human conclusions. The exactness of various strategies is basically inspected keeping in mind the end goal to get to their execution on the premise of parameters, e.g. accuracy, review, f-measure, and precision.

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Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Computational Intelligence and Neuroscience ◽

10.1155/2020/7980434 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Hye-Jin Kim ◽

Sung Min Park ◽

Byung Jin Choi ◽

Seung-Hyun Moon ◽

Yong-Hyuk Kim

Keyword(s):

Machine Learning ◽

Time Series ◽

Quality Control ◽

Mean Squared Error ◽

Machine Learning Algorithms ◽

Support Vector ◽

Weather Element ◽

Applied Machine Learning ◽

Squared Error ◽

Atmospheric Data

We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

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