Cyber bullying detection and analysis.ppt.pdf
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The document discusses the influence of social media on consumer opinions and the development of a content-based sequential opinion influence framework for sentiment prediction using historical and current data. It highlights the limitations of existing models which often overlook the importance of content and long-term data, proposing new models utilizing CNN, LSTM, and TCNN that have shown superior performance in experiments. Additionally, it outlines various applications and methodologies in sentiment analysis, including preprocessing techniques, classification processes, and the significance of machine learning in understanding and predicting user sentiments.
![ [1] Daniele Cenni, Paolo Nesi, Gianni Pantaleo, Imad Zaza., “Twitter
vigilance: A multi-user platform for cross-domain Twitter data analytics, NLP
and sentiment analysis”, In Proceedings of the IEEE International Conference.
[2] H. Sankar, V. Subramaniyaswamy, "Investigating Sentiment Analysis Using
Machine Learning Approach", International Conference on Intelligent
Sustainable Systems (ICISS) (2017).
[3] Lavika Goel, Anurag Prakash, “Sentiment Analysis of Online Communities
Using Swarm Intelligence Algorithms”, 2016 8th International Conference on
Computational Intelligence and Communication Networks (CICN).
[4] Lu Ma, Dan Zhang, Jian-wu Yang, Xiong Luo ., “Sentiment Orientation
Analysis Of Short Text Based On Background And Domain Sentiment Lexicon
Expansion”, 2016 5th International Conference on Computer Science and
Network Technology (ICCSNT).
[5] Shokoufeh Salem Minab, Mehrdad Jalali, Mohammad Hossein Moattar,
“Online Analysis Of Sentiment On Twitter”, 2015 International Congress on
Technology, Communication and Knowledge (ICTCK).](https://image.slidesharecdn.com/cyberfinalppt-240420102556-ee728a32/85/Cyber-bullying-detection-and-analysis-ppt-pdf-26-320.jpg)
Cyber bullying detection and analysis.ppt.pdf
- 2. Nowadays, social media has become a popular channel for people to exchange opinions through the user-generated text. Exploring the mechanisms about how customers' opinions towards products are influenced by friends, and further predicting their future opinions have attracted great attention from corporate administrators and researchers. Various influence models have already been proposed for the opinion prediction problem. However, they largely formulate opinions as derived sentiment categories or values but ignore the role of the content information. Besides, existing models only make use of the most recently received information without taking into consideration the long-term historical communication. To keep track of user opinion behaviors and infer user opinion influence from the historical exchanged textual information, we develop a content-based sequential opinion influence framework. Based on this framework, two opinion sentiment prediction models with alternative prediction strategies are proposed. In the experiments conducted on Twitter datasets, the proposed models outperform other popular influence models. An interesting finding based on a further analysis of user characteristic is that an individual's influence is correlated to her/his style of expressions. In our project we will be using CNN+LSTM as existing and Tree Convolution Neural Network (TCNN) as proposed system.
- 3. Classification of opinion is becoming very important as they play a vital role in decision making. Sentiments expressed via micro blogging sites such as Instagram, twitter, Facebook, etc. helps in understandingthe mindset of people. Several models have been proposed to investigate and extract distinct opinionsfrom users where the accuracy is important. Text mining and data mining helps to identify patterns and establish relationshipsto solve problemsin analyzing large datasets. Tools that are used for data mining allow commercial enterprises to draw conclusionfor future trends. We propose a content-based sequential opinion influence framework to incorporate the content information with the historical information for sentiment prediction in opinion dynamics.
- 4. Machine Learning: In the statistical context, Machine Learning is defined as an application of artificial intelligence where available information is used through algorithms to process or assist the processing of statistical data. While Machine Learning involves concepts of automation, it requires human guidance. Machine Learning involves a high level of generalization in order to get a system that performs well on yet unseen data instances. Machine learning is a relatively new discipline within Computer Science that provides a collectionof data analysis techniques. Some of these techniques are based on well established statistical methods (e.g. logistic regression and principal component analysis) while many others are not.
- 5. Increase the efficiency in predicting the hatred speech in good percentage. Less time consumption. User friendly and should be applicable to all datasets. More accuracy rate.
- 6. S.No Title/Year Objective Pros Cons 1 Non-Fungible Tokens: What Makes Them Valuable? 2023 Non-Fungible Tokens (NFTs) have revolutionized various industries and aspects of the digital world in several ways. Built on blockchain technology, Good architectural work Cannot be implemented in all datasets. 2 Dual Sentiment Classification with Sarcasm Identification 2019 Dual Sentiment Classification (DSC) with Sarcasm Identification Good accuracy rate High computational cost 3 Twitter Sentiment Analysis for COVID-19 2021 RoBERTa(Robustly Optimised BERT pre- training Approach) and CNN-RoBERTa Sentiment Extraction User friendl model Training words is difficult
- 7. S.No Title/Year Objective Pros Cons 4 Discriminative Machine Association Learning based on the Optimized Feature Analysis Adaptive Spider Foraging Model for twitter sentiment analysis 2023 he tweets and messages generated from a social network with Tweet term features Good classification Preprocessing stage is not good 5 Sentence Patterns based Sarcasm Detection and Classification using Long Short Term Memory Model 2022 ong-Short Term Memory (LSTM), along with GLoVe embeddings Good accuracy rate High computational cost
- 8. S.No Title/Year Objective Pros Cons 7 Shaming tweets detection on Twitter using Machine learning Algorithms 2022 Random Forest, Naive Bayes, KNN Good architectural work Cannot be implemented in all datasets. 8 BREE-HD: A Transformer-Based Model to Identify Threats on Twitter 2023 BREE-HD performs extraordinarily well with an accuracy of 97% Good accuracy rate High computational cost 9 Sarcasm Detection and Quantification in Arabic Tweets 2021 unique in sarcasm detection and the proposed approach tackles the problem as a regression problem instead of classification; User friendly model Training words is difficult
- 9. S.No Title/Year Objective Pros Cons 10 Sarcasm detection of non # tagged statements using MLP-BP 2020 anger, joy, contradict, humour etc but people frame the sarcasm to hide actual meaning Good architectural work Cannot be implemented in all datasets.
- 10. Convolution Neural Network: In Machine learning, a convolutional neural network (CNN, or ConvNet) is a class of Machine neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "fully-connectedness" of these networks makes them prone to overfitting data. Typical ways of regularization include adding some form of magnitude measurement of weights to the loss function. CNNs take a different approach towards regularization: they take advantage of the hierarchical pattern in data and assemble more complex patterns using smaller and simpler patterns. Therefore, on the scale of connectedness and complexity, CNNs are on the lower extreme.
- 11. Long Short Term Memory (LSTM): Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of Machine learning. Unlike standard feed forward neural networks, LSTM has feedback connections. It can not only process single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems). A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell remembers values over arbitrary time intervals and the three gates regulate the flow of information into and out of the cell. LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series.
- 12. The current work only considers the influence from neighbors. So, to extend capture the influence of the external information sources on users opinion behaviors. Sentence extraction difficult to find the optimal opinions. Redundancy occur is more. Finding opinion target extraction and opinion summarization is very hard.
- 13. Tree Convolution Neural Network (Tree CNN): A Convolution Neural Network (CNN) is a class of artificial neural networks where connections between nodes form a graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feed forward neural networks, CNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connectedhandwritingrecognition or speech recognition. The term “Enhanced neural network” is used indiscriminately to refer to two broad classes of networks with a similar general structure, where one is finite impulse and the other is infinite impulse. Both classes of networks exhibit temporal dynamic behavior. A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feed forward neural network, while an infinite impulse recurrent network is a directed cyclic graph that cannot be unrolled.
- 14. In propose a novel cross-domain sentiment classification algorithm and content based sentiment analysis algorithm based on term frequency, to analyze the sentiment polarity for short texts. It expand feature vectors based on unlabeled data from the target domain. In this way, some important sentiment indicators for the target domain are appended to feature vectors. At last, validation of algorithm on one target dataset by using two typical datasets. The project, mainly focus on positive and negative sentiment reviews. The first strategy is to identify the reviews as positive or negative by using the positive and negative words used in the review comments. Then expand features based on the co- occurrence frequency between a candidate of additional related feature and a domain-independent feature. Compared with point wise mutual information, sentiment related index considers the distributions of word occurrences instead of the co- occurrence frequency between different words, thus surmounting the challenge caused by infrequent features and words. Then calculate weightage and ranking in each opinions using content analysis algorithm.
- 15. Sentence extraction is not difficult to find the optimal opinions. Redundancy occur is less. Finding opinion target extraction and opinion summarization is very easy.
- 16. Processor: I3 Processor. Ram: 8 GB Hard Disk: 500 G.B Hard Disk 14 inch monitor
- 17. Operating System: Windows 7/8/10 Technology: Python Languages used: Python
- 19. Proposed algorithm Tree CNN is a new one and not implemented before.
- 22. A. Instagram/Twitter API User needs to register first by giving his/her own information. While registering user should give their exact current location. If he/she is giving wrong location means, he is not supposed to register and login. And that user will be considered as a blocked user. So user needs to give only the current location. After registration user will login with username and password. Then he/she can see their profile and can view all user tweets. Admin needs to login with username and password. If both match, he/she will be considered as a valid person. After login, admin can view all blocked user who gave wrong location while registration. Admin can able to see all users profile and tweets. B. Post contents In this module registered user can post tweets in insta. If the user tries to post any tweet which contains bad words means, it will not get posted in the instagram account. So the algorithm will restrict the user not to post bad words. The general posts can be posted in application and as well as in insta.
- 23. C. Search Query Here in this module, user can search for any query in the application. The query has been processed and extracted live tweets from the real time twitter. The Keywords related 100 tweets are extracted from the live twitter. D. Preprocessing In this step all the tweets are extracted from twitter are processed and the noise data are removed. 1) Stop words Removal: A dictionary based approach is been utilized to remove stop words from tweets. A generic stop word list containing 75 stop words created using hybrid approach is used. The algorithm is implemented as below given steps. The target text is tokenized and individual words are stored in array. A single stop word is read from stop word list. The stop word is compared to target text in form of array using sequential search technique. If it matches, the word in array is removed, and the comparison is continued till length of array. After removal of stop word completely, another stop word is read from stop word list and again algorithm runs continuously until all the stop words are compared. Resultant text devoid of stop words is displayed, also required statistics like stop word removed, no. of stop words removed from target text, total count of words in target text, count of words in resultant text, individual stop word count found in target text is displayed.
- 24. 2) Stemming Technique: After removing the unwanted words from the tweet, stemming technique is processed. Stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form generally a written word form. The stem need not be identical to the morphological root of the word; it is usually sufficient that related words map to the same stem, even if this stem is not in itself a valid root. E. Classification After stemming process, all the tweet terms containing the keyword are classified into positive, negative and neutral tweets. CNN, LSTM, TCNN is used for classification. Here we are having good words and bad words datasets. By comparing with this, we can classify the posts into positive, negative and neutral tweets.
- 25. The experiments conducted on the Twitter dataset demonstrate the effectiveness of the two proposed models. The prediction ability of the proposed model is further verified on the opinion word prediction task. Based on the learned influence, we explore the expression styles of users with different influence powers, which provide the valuable information for people to manage their accounts and design marketing plans.
- 26. [1] Daniele Cenni, Paolo Nesi, Gianni Pantaleo, Imad Zaza., “Twitter vigilance: A multi-user platform for cross-domain Twitter data analytics, NLP and sentiment analysis”, In Proceedings of the IEEE International Conference. [2] H. Sankar, V. Subramaniyaswamy, "Investigating Sentiment Analysis Using Machine Learning Approach", International Conference on Intelligent Sustainable Systems (ICISS) (2017). [3] Lavika Goel, Anurag Prakash, “Sentiment Analysis of Online Communities Using Swarm Intelligence Algorithms”, 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN). [4] Lu Ma, Dan Zhang, Jian-wu Yang, Xiong Luo ., “Sentiment Orientation Analysis Of Short Text Based On Background And Domain Sentiment Lexicon Expansion”, 2016 5th International Conference on Computer Science and Network Technology (ICCSNT). [5] Shokoufeh Salem Minab, Mehrdad Jalali, Mohammad Hossein Moattar, “Online Analysis Of Sentiment On Twitter”, 2015 International Congress on Technology, Communication and Knowledge (ICTCK).