Reviewerly/exHarmony huggingface dataset:
The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying

In a world of ever-growing customer data, businesses are required to have a clear line of sight into what their customers think about the business, its products, people and how it treats them. Insight into these critical areas for a business will aid in the development of a robust customer experience strategy and in turn drive loyalty and recommendations to others by their customers. It is key for business to access and mine their customer data to drive a modern customer experience. This article investigates the use of a text mining approach to aid sentiment analysis in the pursuit of understanding what customers are saying about products, services and interactions with a business. This is commonly known as Voice of the Customer (VOC) data and it is key to unlocking customer sentiment. The authors analyse the relationship between unstructured customer sentiment in the form of verbatim feedback and structured data in the form of user review ratings or satisfaction ratings to explore the question of whether customers say what they really think when given the opportunity to provide free text feedback as opposed to how they rate a product on a scale of one to five. Using various Sentiment Analysis approaches, the authors assign a sentiment score to a piece of verbatim feedback and then categorise it as positive, negative, or neutral. Using this normalised sentiment score, they compare it to the corresponding rating score and investigate the potential business insights. The results obtained indicate that a business cannot rely solely on a standalone single metric as a source of truth regarding customer experience. There is a significant difference between the customer ratings score and the sentiment of their corresponding review of the product. The authors propose that it is imperative that a business supplements their customer feedback scores with a robust sentiment analysis strategy.
A Novel Algorithm for Sentiment Analysis of Online Movie Reviews

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.
Opinion Mining of Twitter Events using Supervised Learning

Micro-blogs are a powerful tool to express an opinion. Twitter is one of the fastest growing micro-blogs and has more than 900 million users. Twitter is a rich source of opinion as users share their daily experience of life and respond to specific events using tweets on twitter. In this article, an automatic opinion classifier capable of automatically classifying tweets into different opinions expressed by them is developed. Also, a manually annotated corpus for opinion mining to be used by supervised learning algorithms is designed. An opinion classifier uses semantic, lexical, domain dependent, and context features for classification. Results obtained confirm competitive performance and the robustness of the system. Classifier accuracy is more than 75.05%, which is higher than the baseline accuracy.
Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis

The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a large scale on social networking sites. These social media data are available in an unstructured form, which includes images, text, and videos. The processing of these data is difficult, but some sentiment analysis, information retrieval, and recommender systems are used to process these unstructured data. To extract the opinion and sentiment of internet users from their written social media text, a sentiment analysis system is required to develop, which can work on both monolingual and bilingual phonetic text. Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain datasets. The system performance is tested on four different datasets and achieved better accuracy of 3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56% on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for verbs of the English language is proposed, which improves the SA system's performance.
Effectiveness of Normalization Over Processing of Textual Data Using Hybrid Approach Sentiment Analysis

Various natural language processing tasks are carried out to feed into computerized decision support systems. Among these, sentiment analysis is gaining more attention. The majority of sentiment analysis relies on the social media content. This web content is highly un-normalized in nature. This hinders the performance of decision support system. To enhance the performance, it is required to process data efficiently. This article proposes a novel method of normalization of web data during the pre-processing phase. It is aimed to get better results for different natural language processing tasks. This research applies this technique on data for sentiment analysis. Performance of different learning models is analysed using precision, recall, f-measure, fallout for normalize and un-normalize sentiment analysis. Results shows after normalization, some documents shift their polarity i.e. negative to positive. Experimental results show normalized data processing outperforms un-normalized data processing with better accuracy.
Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media

A recent British study of people between the ages of 14 and 35 has shown that social media has a negative impact on mental health. The purpose of the paper is to detect people with mental disorders' behavior in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia, etc. For this, the author used text mining and machine learning algorithms (naïve Bayes, k-nearest neighbours) to analyse tweets. The obtained results were validated using different evaluation measures such as f-measure, recall, precision, entropy, etc.
A Survey on Aspect Extraction Approaches for Sentiment Analysis

Aspect-level sentiment analysis gives a detailed view of user opinions expressed towards each feature of a product. Aspect extraction is a challenging task in aspect-level sentiment analysis. Hence, several researchers worked on the problem of aspect extraction during the past decade. The authors begin this chapter with a brief introduction to aspect-level sentimental analysis, which covers the definition of key terms used in this chapter, and the authors also illustrate various subtasks of aspect-level sentiment analysis. The introductory section is followed by an explanation of the various feature learning methods like supervised, unsupervised, semi-supervised, etc. with a discussion regarding their merits and demerits. The authors compare the aspect extraction methods performance with respect to metrics and a detailed discussion on the merits and demerits of the approaches. They conclude the chapter with pointers to the unexplored problems in aspect-level sentiment analysis that may be beneficial to the researchers who wish to pursue work in this challenging and mature domain.
Feature Optimization in Sentiment Analysis by Term Co-occurrence Fitness Evolution (TCFE)

The opinion of a target audience is a major objective for the assessing state of efficacy pertaining to reviews, business decisions surveys, and such factors that require decision making. Feature selection turns out to be a critical task for developing robust and high levels of classification while decreasing training time. Models are required for stating the scope for depicting optimal feature selection for escalating feature selection strategies to escalate maximal accuracy in opinion mining. Considering the scope for improvement, an n-gram feature selection approach is proposed where optimal features based on term co-occurrence fitness is proposed in this article. Genetic algorithms focus on determining the evolution and solution to attain deterministic and maximal accuracy having a minimal level of computational process for reflecting on the sentiment scope for sentiment. Evaluations reflect that the proposed solution is capable, which outperforms the separate filter-oriented feature selection models of sentiment classification.
Sentiment Analysis of Tweets on the COVID-19 Pandemic Using Machine Learning Techniques

Sentiment evaluation alludes to separate the sentiments from the characteristic language and to perceive the mentality about the exact theme. Novel corona infection, a harmful malady ailment, is spreading out of the blue through the quarter, which thought processes respiratory tract diseases that can change from gentle to extraordinary levels. Because of its quick nature of spreading and no conceived cure, it ushered in a vibe of stress and pressure. In this chapter, a framework perusing principally based procedure is utilized to discover the musings of the tweets related to COVID and its effect lockdown. The chapter examines the tweets identified with the hash tags of crown infection and lockdown. The tweets were marked fabulous, negative, or fair, and a posting of classifiers has been utilized to investigate the precision and execution. The classifiers utilized have been under the four models which incorporate decision tree, regression, helpful asset vector framework, and naïve Bayes forms.
A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis

In recent years, increasing attention is being paid to sentiment analysis on microblogging platforms such as Twitter. Sentiment analysis refers to the task of detecting whether a textual item (e.g., a tweet) contains an opinion about a topic. This paper proposes a probabilistic deep learning approach for sentiments analysis. The deep learning model used is a convolutional neural network (CNN). The main contribution of this approach is a new probabilistic representation of the text to be fed as input to the CNN. This representation is a matrix that stores for each word composing the message the probability that it belongs to a positive class and the probability that it belongs to a negative class. The proposed approach is evaluated on four well-known datasets HCR, OMD, STS-gold, and a dataset provided by the SemEval-2017 Workshop. The results of the experiments show that the proposed approach competes with the state-of-the-art sentiment analyzers and has the potential to detect sentiments from textual data in an effective manner.
Develop a Neural Model to Score Bigram of Words Using Bag-of-Words Model for Sentiment Analysis

A Bag-of-Words model is widely used to extract the features from text, which is given as input to machine learning algorithm like MLP, neural network. The dataset considered is movie reviews with both positive and negative comments further converted to Bag-of-Words model. Then the Bag-of-Word model of the dataset is converted into vector representation which corresponds to a number of words in the vocabulary. Each word in the review documents is assigned with a score and the scores are later represented in vector representation which is later fed as input to neural model. In the Kera's deep learning library, the neural models will be simple feedforward network models with fully connected layers called ‘Dense'. Bigram language models are developed to classify encoded documents as either positive or negative. At first, reviews are converted to lines of token and then encoded to bag-of-words model. Finally, a neural model is developed to score bigram of words with word scoring modes.
Integrating Feature and Instance Selection Techniques in Opinion Mining

Opinion mining focuses on extracting polarity information from texts. For textual term representation, different feature selection methods, e.g. term frequency (TF) or term frequency–inverse document frequency (TF–IDF), can yield diverse numbers of text features. In text classification, however, a selected training set may contain noisy documents (or outliers), which can degrade the classification performance. To solve this problem, instance selection can be adopted to filter out unrepresentative training documents. Therefore, this article investigates the opinion mining performance associated with feature and instance selection steps simultaneously. Two combination processes based on performing feature selection and instance selection in different orders, were compared. Specifically, two feature selection methods, namely TF and TF–IDF, and two instance selection methods, namely DROP3 and IB3, were employed for comparison. The experimental results by using three Twitter datasets to develop sentiment classifiers showed that TF–IDF followed by DROP3 performs the best.
Challenges of Text Analytics in Opinion Mining

Text analysis is the task of knowledge distillation from unstructured text. Due to increase in sharing of information over the web in text format, users required tools and techniques for the analysis of the text. These techniques can be used in two ways: One, this can be used for clustering, classification, and visualization of the data. Two, this can be used for predicting the future aspects, for example, in share market. But all these tasks are not easy to perform, as there are lots of challenges in converting the text into the format onto which various actions can be taken. In this chapter, the authors have discussed the framework of text analysis, followed by the background where they have discussed the steps for transforming the text into the structured form. They have shed light on its industry application along with the technological and non-technological challenges in text analysis.
Parallel Hybrid BBO Search Method for Twitter Sentiment Analysis of Large Scale Datasets Using MapReduce

Sentiment analysis is an eminent part of data mining for the investigation of user perception. Twitter is one of the popular social platforms for expressing thoughts in the form of tweets. Nowadays, tweets are widely used for analyzing the sentiments of the users, and utilized for decision making purposes. Though clustering and classification methods are used for the twitter sentiment analysis, meta-heuristic based clustering methods has witnessed better performance due to subjective nature of tweets. However, sequential meta-heuristic based clustering methods are computation intensive for large scale datasets. Therefore, in this paper, a novel MapReduce based K-means biogeography based optimizer(MR-KBBO) is proposed to leverage the strength of biogeography based optimizer with MapReduce model to efficiently cluster the large scale data. The proposed method is validated against four state-of-the-art MapReduce based clustering methods namely; parallel K-means, parallel K-means particle swarm optimization, MapReduce based artificial bee colony optimization, dynamic frequency based parallel k-bat algorithm on four large scale twitter datasets. Further, speedup measure is used to illustrate the computation performance on varying number of nodes. Experimental results demonstrate that the proposed method is efficient in sentiment mining for the large scale twitter datasets.
A Machine Learning-Based Lexicon Approach for Sentiment Analysis

Sentiment analysis can be a very useful aspect for the extraction of useful information from text documents. The main idea for sentiment analysis is how people think for a particular online review, i.e. product reviews, movie reviews, etc. Sentiment analysis is the process where these reviews are classified as positive or negative. The web is enriched with huge amount of reviews which can be analyzed to make it meaningful. This article presents the use of lexicon resources for sentiment analysis of different publicly available reviews. First, the polarity shift of reviews is handled by negations. Intensifiers, punctuation and acronyms are also taken into consideration during the processing phase. Second, words are extracted which have some opinion; these words are then used for computing score. Third, machine learning algorithms are applied and the experimental results show that the proposed model is effective in identifying the sentiments of reviews and opinions.
Open Issues in Opinion Mining

Opinions are found everywhere. In web forums like social networking websites, e-commerce sites, etc., rich user-generated content is available in large volume. Web 2.0 has made rich information easily accessible. Manual insight extraction of information from these platforms is a cumbersome task. Deriving insight from such available information is known as opinion mining (OM). Opinion mining is not a single-stage process. Text mining and natural language processing (NLP) is used to obtain information from such data. In NLP, content from the text corpus is pre-processed, opinion word is extracted, and analysis of those words is done to get the opinion. The volume of web content is increasing every day. There is a demand for more ingenious techniques, which remains a challenge in opinion mining. The efficiency of opinion mining systems has not reached the satisfactory level because of the issues in various stages of opinion mining. This chapter will explain the various research issues and challenges present in each stage of opinion mining.
An Approach to Opinion Mining in Community Graph Using Graph Mining Techniques

Opinions are the central theme to almost all human activities, as well the key influencers of our behaviours. Opinions related to sentiments, evaluations, attitudes, and emotions are the features of studying of opinion mining. It is important to study peoples of various communities sentiments about the schemes implemented by the government agencies as well as NGOs. The opinion mining is about the opinions of various communities of villages of a Panchayat about various social schemes implemented by the government of India. This article proposes an algorithm for opinion mining in a community graph for various social schemes run by the Panchayat using graph mining techniques. The algorithm has been implemented in C++ programming language.
An Overview of Methodologies and Challenges in Sentiment Analysis on Social Networks

The expeditious increase in the adoption of social media over the last decade, determining and analyzing the attitude and opinion of masses related to a particular entity, has gained quite an importance. With the landing of the Web 2.0, many internet products like blogs, community chatrooms, forums, microblog are serving as a platform for people to express themselves. Such opinion is found in the form of messages, user-comments, news articles, personal blogs, tweets, surveys, status updates, etc. With sentiment analysis, it is possible to eliminate the need to manually going through each and every user comment by focusing on the contextual polarity of the text. Analyzing the sentiments could serve a number of applications like advertisements, recommendations, quality analysis, monetization provided on the web services, real-time analysis of data, analyzing notions related to candidates during election campaign, etc.
Impact of Balancing Techniques for Imbalanced Class Distribution on Twitter Data for Emotion Analysis

Continuously growing technology enhances creativity and simplifies humans' lives and offers the possibility to anticipate and satisfy their unmet needs. Understanding emotions is a crucial part of human behavior. Machines must deeply understand emotions to be able to predict human needs. Most tweets have sentiments of the user. It inherits the imbalanced class distribution. Most machine learning (ML) algorithms are likely to get biased towards the majority classes. The imbalanced distribution of classes gained extensive attention as it has produced many research challenges. It demands efficient approaches to handle the imbalanced data set. Strategies used for balancing the distribution of classes in the case study are handling redundant data, resampling training data, and data augmentation. Six methods related to these techniques have been examined in a case study. Upon conducting experiments on the Twitter dataset, it is seen that merging minority classes and shuffle sentence methods outperform other techniques.
Fault-tolerant quantum error correction

This chapter is devoted to fault-tolerant quantum error correction concepts. The chapter starts with an introduction to fault-tolerance basics and traversal operations. It continues with fault-tolerant quantum information processing concepts and procedures. The universal set of fault-tolerant quantum gates is introduced, followed by fault-tolerant measurement, fault-tolerant state preparation, and fault-tolerant encoding state preparation, using Steane code as an example. The focus then moves to a rigorous description of fault-tolerant quantum error correction. After a short review of some basic concepts from stabilizer codes, fault-tolerant syndrome extraction is described, followed by a description of fault-tolerant encoding operations. Next described is the application of quantum gates on a quantum register through a measurement protocol. When applied transversally, this method enables fault-tolerant error correction based on arbitrary quantum stabilizer code. Fault-tolerant stabilizer codes are described next. Finally, the [5,1,3] fault-tolerant stabilizer code is described as an example.
Quantum sensing and quantum radars

This chapter is devoted to quantum sensing and quantum radars. It begins with a quantum phase estimation problem and then describes the physical limits of phase measurements. Quantum interferometry is described next; when applied to the separable quantum probe, the standard quantum limit is obtained. When highly entangled NOON states are used as quantum probes, the Heisenberg limit is achieved. Quantum Fisher information is introduced and used to derive the quantum Cramér–Rao bound, representing the lower bound for quantum sensing precision. The focus then moves to distributed quantum sensing, in which multiple quantum sensors are used to measure a global parameter. Optimum entangled probes are shown to overcome the standard quantum limit and achieve the Heisenberg limit. We also describe how distributed quantum sensing can be used to determine quadrature displacements. Performance limits are derived as well. In the section on quantum radars, common quantum radar architectures are described, after which several promising quantum radar technologies are described: interferometric quantum radars, quantum illumination-based quantum radars, and entanglement-assisted target detection. The section on quantum radars concludes with the quantum radar equation.