Deep Convolution Neural Networks for Twitter Sentiment Analysis

Jianqiang, Zhao; Gui, Xiaolin; Zhang, Xuejun

doi:10.1109/access.2017.2776930

Cited by 394 publications

(200 citation statements)

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“…Recall (R)/ Sensitivity (Sn) It defines the sensitivity or the completeness of any classifier. 27-29,31-33,35,37-39,43-45,47,49-51,57-59, A higher recall value indicates less ''false positives'' and vice 61,62,64,66,68,70,74,75,81,83,84 versa. Recall and precision are bounded by inverse relation with each other.…”

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confidence: 99%

“…Accuracy (A) It is defined as proximity of a measurement to its true value. It 17,29,40,43-45,48-50,52,55,57,60,64, is calculated as a proportion of TP and true negatives (TN) 66,[68][69][70][72][73][74][75][77][78][79]85,[81][82][83] among total inspected cases.…”

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“…F score/F1 score/ It is defined as ''weighted'' harmonic mean of Recall and 27-35,37-39,43,44,47,49-51,53,54,59,61,62, F measure/F1 measure/F1 value(F) Precision. It is the combination of the precision and recall [66][67][68]70,74,75,81,83,84 measures.…”

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confidence: 99%

“…The SemEval 200730 dataset comprises of tweets belonging to the news headlines of the major newspapers consisting of 1250 headlines. The SemEval 2013 is available at http://www.cs.york.ac.uk/semeval-2013/ task2/ and consists of 6,237, 1654 and 3813 manually annotated tweets in the training, development and test sets 52,55,75,76,83. The SemEval 2014 dataset includes data related to ten different tasks evaluating varied ''computational semantic systems,'' provided by the SemEval 2014 conference 52,73,83.…”

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confidence: 99%

“…The SemEval 2013 is available at http://www.cs.york.ac.uk/semeval-2013/ task2/ and consists of 6,237, 1654 and 3813 manually annotated tweets in the training, development and test sets 52,55,75,76,83. The SemEval 2014 dataset includes data related to ten different tasks evaluating varied ''computational semantic systems,'' provided by the SemEval 2014 conference 52,73,83. The SemEval 201554,83 is available at http://alt.qcri.org/semeval2015/task11 and consists of tweets which are enriched with metaphors and ironical content.…”

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Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Kumar

Jaiswal

2019

Concurrency and Computation

133

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Sentiment detection and classification is the latest fad for social analytics on Web. With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non-trivial. With a marked increase in the size, subjectivity, and diversity of social web-data, the vagueness, uncertainty and imprecision within the information has increased manifold. Soft computing techniques have been used to handle this fuzziness in practical applications. This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter. We present a systematic literature review to collate, explore, understand and analyze the efforts and trends in a well-structured manner to identify research gaps defining the future prospects of this coupling. The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter and secondly as compared to the previous reviews we adopt a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high-quality studies to address specific research questions pertaining to the defined research domain. KEYWORDS machine learning, review, sentiment analysis, soft computing, Twitter INTRODUCTIONThe incessantly evolving dynamics of the Web in terms of the volume, velocity and variety of opinion-rich information accessible online, has made research in the domain of Sentiment Analysis (SA) a trend for many practical applications which facilitate decision support and deliver targeted information to domain analysts. Interestingly, the buzzing term ''big data'' which is estimated to be 90% unstructured 1 further makes it crucial to tap and analyze information using contemporary tools. Text mining models define the process to transform and substitute this unstructured data into a structured one for knowledge discovery. Use of classification algorithms to intelligently mine text has been studied extensively across literature. 2,3 SA, established as a typical text classification task, 4 is defined as the computational study of people's opinions, attitudes and emotions towards an entity. 5,6 It offers a technology-based solution to understand people's reactions, views and opinion polarities (positive, negative or neutral) in textual content available over social media sources.Research studies and practical applications in the field of SA have escalated in the past decade with the transformation and expansion of Web from passive provider of content to an active socially-aware distributor of collective intelligence. This new collaborative Web (called Web 2.0), 7 extended by Web-based technologies like comments, blogs and wikis, social media portals like Twitter or Facebook, that allow to build social networks based on professional relationship, i...

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Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Kumar

Jaiswal

2019

Concurrency and Computation

133

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An unknown protocol syntax analysis method based on convolutional neural network

Wang

Bai

Hei

et al. 2020

Trans Emerging Tel Tech

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In recent years, a large number of botnets and dark networks rely on command and control channels of unknown protocol formats for communication, and with the development of Internet of Things technology, this problem becomes more prominent. The syntax analysis of the unknown protocol is helpful to measure the boundary of Botnet in the environment of Internet of things, so as to protect the network security. Based on the analysis of the characteristics of the current bitstream protocol data format, this article proposes an unknown protocol syntax analysis method based on convolutional neural network (CNN). First, the protocol data are preprocessed, and then the image is transformed. Next, the converted image is input to the convolution layer for convolution. After convolution, the data are flattened. Then the flattened data are put into the fully connected neural network. Finally, the unknown protocol is analyzed and predicted. The experimental results show that compared with the traditional feature extraction combine frequent item algorithm (CFI) and other neural network deep neural networks, CNN is 15% more accurate than CFI in the analysis of unknown protocol syntax, and it can accurately analyze and identify the unknown protocol.

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Improving the emotion‐based classification by exploiting the fuzzy entropy in FCM clustering

Cardone

Martino

Senatore

2021

Int J of Intelligent Sys

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Emotion detection in the natural language text has drawn the attention of several scientific communities as well as commercial/marketing companies: analyzing human feelings expressed in the opinions and feedback of web users helps understand general moods and support market strategies for product advertising and market predictions. This paper proposes a framework for emotion‐based classification from social streams, such as Twitter, according to Plutchik's wheel of emotions. An entropy‐based weighted version of the fuzzy c‐means (FCM) clustering algorithm, called EwFCM, to classify the data collected from streams has been proposed, improved by a fuzzy entropy method for the FCM center cluster initialization. Experimental results show that the proposed framework provides high accuracy in the classification of tweets according to Plutchik's primary emotions; moreover, the framework also allows the detection of secondary emotions, which, as defined by Plutchik, are the combination of the primary emotions. Finally, a comparative analysis with a similar fuzzy clustering‐based approach for emotion classification shows that EwFCM converges more quickly with better performance in terms of accuracy, precision, and runtime. Finally, a straightforward mapping between the computed clusters and the emotion‐based classes allows the assessment of the classification quality, reporting coherent and consistent results.

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Deep Convolution Neural Networks for Twitter Sentiment Analysis

Cited by 394 publications

References 24 publications

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

An unknown protocol syntax analysis method based on convolutional neural network

Improving the emotion‐based classification by exploiting the fuzzy entropy in FCM clustering

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