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Book ChapterDOI

Sentiment analysis of the #MeToo movement using neutrosophy: Application of single-valued neutrosophic sets

01 Jan 2020-pp 117-135
TL;DR: This chapter provides a modern-day real-world application of Neutrosophy in sentiment analysis of the #MeToo movement and increases the accuracy in predicting the indeterminate polarity.
Abstract: Single-valued neutrosophic sets (SVNSs) have been used in scientific problems but not in sociological analysis. This chapter provides a modern-day real-world application of Neutrosophy in sentiment analysis of the #MeToo movement. Sentiment analysis categorizes people's opinions as positive or negative, and the neutral part is generally ignored even in fuzzy sentiment analysis. To capture the prevailing indeterminate feelings, Neutrosophy is used. Over 400,000 tweets of the #MeToo movement were separately represented with positive, indeterminate, negative memberships as an SVNS, which gives an accurate evaluation of the tweets. Clustering of these tuples into three major clusters using a K-means algorithm displays indeterminate as the largest cluster. To increase the accuracy in predicting the indeterminate polarity, the data was further classified into eight classes. Training data was used to model k-nearest neighbor and support vector machine classifiers. A comparative analysis between the classifiers was done.
Citations
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Journal Article
TL;DR: In this article, the authors explore the effect of dimensionality on the nearest neighbor problem and show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance of the farthest data point.
Abstract: We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

1,992 citations

Journal ArticleDOI
TL;DR: A comparative analysis of the methods show that the approach with MRNS provides better refinement to the indeterminacy present in the data.

35 citations

Journal ArticleDOI
18 Oct 2020-Symmetry
TL;DR: A novel framework is proposed that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentimentAnalysis on the text files of those audio.
Abstract: With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithms can be used to classify textual data based on various scales ranging from just positive-negative, positive-neutral-negative to a wide spectrum of emotions. While a lot of work has been done on text, only a lesser amount of research has been done on audio datasets. An audio file contains more features that can be extracted from its amplitude and frequency than a plain text file. The neutrosophic set is symmetric in nature, and similarly refined neutrosophic set that has the refined indeterminacies I1 and I2 in the middle between the extremes Truth T and False F. Neutrosophy which deals with the concept of indeterminacy is another not so explored topic in NLP. Though neutrosophy has been used in sentiment analysis of textual data, it has not been used in speech sentiment analysis. We have proposed a novel framework that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentiment analysis on the text files of those audio.

11 citations


Cites background or methods from "Sentiment analysis of the #MeToo mo..."

  • ...Work on analyzing sentiment of textual data using neutrosophic sets has been sparse and little, only [13,14] made use of SVNS and refined neutrosophic sets for sentiment analysis....

    [...]

  • ...The K-means algorithms used for clustering SVNS values for sentiment analysis was proposed in [13]....

    [...]

  • ...In [13] a detailed comparison between fuzzy logic and neutrosophic logic was shown by analyzing the #metoo movement....

    [...]

Journal ArticleDOI
TL;DR: This work aims to combine the power of deep learning with SVNS to represent a sample’s sentiment into membership functions of SVNS, a novel framework that can integrate with any neural network model and quantify sentiments using SVNS.
Abstract: Deep learning is advancing rapidly; it has aided in solving problems that were thought impossible. Natural language understanding is one such task that has evolved with the advancement of deep learning systems. There have been several sentiment analysis attempts, but they aim to classify it as a single emotion. Human emotion in natural language is generally a complex combination of emotions, which may be indeterminate or neutral at times. Neutrosophy is a branch of philosophy that identifies neutralities and uses membership functions (positive, negative, neutral) to quantify a sample into Single Valued Neutrosophic Set (SVNS) values. Our work aims to combine the power of deep learning with SVNS to represent a sample’s sentiment into membership functions of SVNS. We have worked on the Offensive Language Identification Dataset (OLID). Combining the power of state-of-the-art neural network techniques with neutrosophy allowed us to quantify the sentiments and identify the transition phase between positive and negative ones. We used the transition phase to capture neutral samples, which is beneficial if we want to obtain purely positive/negative samples. We performed experiments using Bi-directional Long Short Term Memory (BiLSTM) with attention, Bidirectional Encoder Representations from Transformers (BERT), A Lite BERT (ALBERT), A Robustly Optimised BERT Approach (RoBERTa), and MPNet. Our SVNS model performed equivalent to state-of-the-art neural network models on the OLID dataset. Here, we propose a novel framework that can integrate with any neural network model and quantify sentiments using SVNS.

10 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a novel framework to implement neutrosophy in deep learning models, where instead of just predicting a single class as output, they quantified the sentiments using three membership functions to understand them better.
Abstract: Deep learning has been widely used in numerous real-world engineering applications and for classification problems. Real-world data is present with neutrality and indeterminacy, which neutrosophic theory captures clearly. Though both are currently developing research areas, there has been little study on their interlinking. We have proposed a novel framework to implement neutrosophy in deep learning models. Instead of just predicting a single class as output, we have quantified the sentiments using three membership functions to understand them better. Our proposed model consists of two blocks, feature extraction, and feature classification. Having a separate feature extraction block enables us to use any model as a feature extractor. We experimented with BiLSTM using GloVe (Global Vectors for word representation), BERT (Bidirectional Encoder Representations from Transformers), ALBERT (A Lite BERT), RoBERTa (Robustly optimized BERT approach), MPNet, and stacked ensemble models. Feature classification performs prediction and dimensionality reduction of features. Experimental analysis was done on the SemEval 2017 Task 4 dataset (Subtask A). We used the intermediate layer features to define membership functions of Single Valued Neutrosophic Sets (SVNS). We used these membership functions for prediction as well. We have compared our models with the top five teams of the task and recent state-of-the-art systems. Our proposed stacked ensemble model achieved the best recall (0.733) score.

10 citations

References
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Book
08 Jul 2008
TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
Abstract: An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

7,452 citations

Proceedings Article
16 May 2014
TL;DR: Interestingly, using the authors' parsimonious rule-based model to assess the sentiment of tweets, it is found that VADER outperforms individual human raters, and generalizes more favorably across contexts than any of their benchmarks.
Abstract: The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a gold-standard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.

3,299 citations

Journal Article
TL;DR: In this article, the authors explore the effect of dimensionality on the nearest neighbor problem and show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance of the farthest data point.
Abstract: We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

1,992 citations

Posted Content
01 Apr 2010-viXra
TL;DR: This work defines the settheoretic operators on an instance of neutrosophic set, and provides various properties of SVNS, which are connected to the operations and relations over SVNS.
Abstract: Neutrosophic set is a part of neutrosophy which studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. Neutrosophic set is a powerful general formal framework that has been recently proposed. However, neutrosophic set needs to be specified from a technical point of view. To this effect, we define the settheoretic operators on an instance of neutrosophic set, we call it single valued neutrosophic set (SVNS). We provide various properties of SVNS, which are connected to the operations and relations over SVNS.

1,408 citations

Proceedings Article
05 Jul 2011
TL;DR: This paper evaluates the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging, and uses existing hashtags in the Twitter data for building training data.
Abstract: In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.

1,261 citations