Application of Optimization and Machine Learning for Sentiment Analysis

Assessment Scores ◽

Opinion Extraction ◽

Learning Techniques ◽

High Level

Sentiment analysis is called detecting emotions extracted from text features and is known as one of the most important parts of opinion extraction. Through this process, we can determine if a script is positive, negative or neutral. In this research, sentiment analysis is performed with textual data. A text feeling analyzer combines natural language processing (NLP) and machine learning techniques to assign weighted assessment scores to entities, subjects, subjects, and categories within a sentence or phrase. In expressing mood, the polarity of text reviews could be graded on a negative to positive scale using a learning algorithm. The current decade has seen significant developments in artificial intelligence, and the machine learning revolution has changed the entire AI industry. After all, machine learning techniques have become an integral part of any model in today's computing world. However, the ensemble to learning techniques is promise a high level of automation with the extraction of generalized rules for text and sentiment classification activities. This thesis aims to design and implement an optimized functionality matrix using to the ensemble learning for the sentiment classification and its applications.

A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.32.16268 ◽

2018 ◽

Vol 7 (2.32) ◽

pp. 462

Author(s):

G Krishna Chaitanya ◽

Dinesh Reddy Meka ◽

Vakalapudi Surya Vamsi ◽

M V S Ravi Karthik

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Buying Behavior ◽

Online Retail ◽

User Reviews ◽

Business Plans ◽

Learning Techniques ◽

User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general.

DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO PREDICT AUTHOR’S AGE FROM TEXT

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v7.i10.2019.408 ◽

2020 ◽

Vol 7 (10) ◽

pp. 380-389

Author(s):

Asogwa D.C ◽

Anigbogu S.O ◽

Anigbogu G.N ◽

Efozia F.N

Keyword(s):

Machine Learning ◽

Language Processing ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Machine Learning Algorithm ◽

Age Group ◽

Political Views ◽

Learning Techniques ◽

Age Prediction

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.

Handbook of Research on Emerging Trends and Applications of Machine Learning - Advances in Computational Intelligence and Robotics ◽

Deep Learning Approaches for Textual Sentiment Analysis

10.4018/978-1-5225-9643-1.ch009 ◽

2020 ◽

pp. 171-182 ◽

Cited By ~ 1

Author(s):

Tamanna Sharma ◽

Anu Bajaj ◽

Om Prakash Sangwan

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Computational Technique ◽

Learning Techniques

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.

Applications of Artificial Intelligence in the Realm of Business Intelligence

Research Anthology on Artificial Intelligence Applications in Security ◽

10.4018/978-1-7998-7705-9.ch018 ◽

2021 ◽

pp. 358-386

Author(s):

Prakhar Mehrotra

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Time Series ◽

Language Processing ◽

Business Intelligence ◽

Current State ◽

Learning Techniques ◽

High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

Advances in Business Information Systems and Analytics - Utilizing Big Data Paradigms for Business Intelligence ◽

Applications of Artificial Intelligence in the Realm of Business Intelligence

10.4018/978-1-5225-4963-5.ch001 ◽

2019 ◽

pp. 1-38

Author(s):

Prakhar Mehrotra

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Time Series ◽

Language Processing ◽

Business Intelligence ◽

Current State ◽

Learning Techniques ◽

High Level

Sentiment Analysis Techniques and Application-Survey and Taxonomy

Engineering and Technology Journal ◽

10.47191/etj/v6i1.01 ◽

2021 ◽

Vol 04 (01) ◽

Author(s):

Mahmood Umar ◽

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Sentiment Classification ◽

Supervised Machine Learning ◽

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.

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 ◽

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.

Machine Learning Techniques for Sentiment Analysis of Indian Languages

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1456.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 3630-3636

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Indian Languages ◽

Indian Language ◽

Learning Techniques ◽

Textual Data ◽

Language Text

Sentiment Analysis is the domain of automatically understanding the emotions, feelings, opinions in a textual data. It is a way of understating how a product, brand, service, idea or an event is viewed by common people, customers and stakeholders. Sentiment Analysis Systems are used by politicians, business leaders, developers and researchers to infer useful information as per their specific needs. It is used in business decision making process to value the views of the customers. Sentiment analysis has become a hot topic of scientific and market research in the field of natural Language Processing. India is a large populated country and the number of Internet users is also huge. Most people share their experience in English. However, during the last decade, due to the accessibility of Internet and evolution in language modelling people express their views in their own native Indian language. With the increase in Indian language text, researchers find it quite fascinating to infer valuable information from this unstructured text data. A number of machine learning techniques have been applied on this textual data set. Basic concepts of Sentiment analysis shall be discussed with focus on Indian language text in this paper. Due to on availability of rich lexicon resources for unsupervised learning techniques and better evaluation measures for the Supervised learning techniques, the later become the first choice for researchers in the field of Natural Language Processing. A comparative analysis shall be made for various supervised machine learning techniques in the context of Indian languages.

A Comprehensive Survey on Aspect Based Word Embedding Models and Sentiment Analysis Classification Approaches

10.3233/apc210175 ◽

2021 ◽

Author(s):

Monika Agrawal ◽

Nageswara Rao Moparthi

Keyword(s):

Sentiment Analysis ◽

Language Processing ◽

Sentiment Classification ◽

Word Embedding ◽

Customer Reviews ◽

Aspect Extraction ◽

Learning Techniques ◽

Entire Sentence ◽

Comprehensive Survey

Sentiment Analysis includes methods and techniques for businesses to understand and analyze customer reviews, feedback and opinion on a particular product or service. Sentiment Analysis uses Natural Language Processing (NLP) tools to analyze feelings or emotions, attitudes, opinions, thoughts, etc. behind the words. Sentiments such as positive, negative and neutral are associated with a particular product. Sentiment analysis is applicable in multi-domains such as customer feedback for a particular product, movie reviews, social and political comments. This survey basically focuses on different aspect-based word embedding models and aspect-based sentiment classification techniques, where the goal is to extract key features from the sentences and classify sentiment on entities at document level. Aspect Based Sentiment Analysis (ABSA) is a technique that concentrates not only the entire sentence but analyses key terms explicitly to predict the polarity as a whole. ABSA model accepts aspect categories and its corresponding aspect terms to generate sentiment corresponding to each aspect from the text corpus. This article provides a comprehensive survey on different word embedding models under CNN framework for aspect extraction and different machine learning techniques applicable for sentiment classification purpose.