Decoding violence against women: analysing harassment in middle eastern literature with machine learning and sentiment analysis Humanities and Social Sciences Communications

Some of the key features provided by Natural Language Toolkit’s libraries include sentence detection, POS tagging, and tokenization. Tokenization, for example, is used in NLP to split paragraphs and sentences into smaller components that can be assigned specific, more understandable, meanings. Python, a high-level, general-purpose programming language, can be applied to NLP to deliver various products, including text analysis applications.

The proposed system adopts this GloVe embedding for deep learning and pre-trained models. Another pretrained word embedding BERT is also utilized to improve the accuracy of the models. The number of social media users is fast growing since it is simple to use, create and share photographs and videos, even among people who are not good with technology. Many websites allow users to leave opinions on non-textual information such as movies, images and animations. YouTube is the most popular of them all, with millions of videos uploaded by users and billions of opinions. Detecting sentiment polarity on social media, particularly YouTube, is difficult.

Evolving linguistic divergence on polarizing social media

Gender harassment is perpetrated to reinforce power imbalances between men and women in Middle Eastern societies. Men often exert dominance over women through verbal abuse or by limiting their access to public spaces (Wei and Asl, 2023). Sprout’s sentiment analysis tools provide real-time insights into customer opinions, helping you respond promptly and appropriately.

It is pretty clear that we extract the news headline, article text and category and build out a data frame, where each row corresponds to a specific news article. However, averaging over all wordvectors in a document is not the best way to build document vectors. Most words in that document are so-called glue words that are not contributing to the meaning or sentiment of a document but rather semantic analysis of text are there to hold the linguistic structure of the text. That means that if we average over all the words, the effect of meaningful words will be reduced by the glue words. From the above obtained results Adapter-BERT performs better for both sentiment analysis and Offensive Language Identification. As Adapter-BERT inserts a two layer fully connected network in each transformer layer of BERT.

Code-mixed data is framed by combining words and phrases from two or more distinct languages in a single text. It is quite challenging to identify emotion or offensive terms in the comments since noise exists in code-mixed data. The majority of advancements in hostile language detection and sentiment analysis are made on monolingual data for languages with high resource requirements.

What is the difference between sentiment analysis and semantic analysis?

Eighty-six percent of the f-measure was attained using the machine learning method. In this study, the SA of Bengali reviews is executed using the word2vec embedding model. Sentiment analysis tools use artificial intelligence and deep learning techniques to decode the overall sentiment, opinion, or emotional tone behind textual data such as social media content, online reviews, survey responses, or blogs.

Experimental results showed that the model outperformed the baselines for all datasets. Recurrent neural networks (RNNs) and their gated variants, Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), have been applied in different NLP tasks such as text generation, sentiment analysis, machine translation, question answering, and summarization. The applications exploit the capability of RNNs and gated RNNs to manipulate inputs composed of sequences of words or characters17,34. RNNs process chronological sequence in both input and output, or only one of them. According to the investigated problem, RNNs can be arranged in different topologies16. In addition to the homogenous arrangements composed of one type of deep learning networks, there are hybrid architectures combine different deep learning networks.

And we can also see that all the metrics fluctuate from fold to fold quite a lot. Compared with the original imbalanced data, we can see that downsampled data has one less entry, which is the last entry of the original data belonging to the positive class. RandomUnderSampler reduces the majority class by randomly removing data from the majority class.

To minimize the risks of translation-induced biases or errors, meticulous translation quality evaluation becomes imperative in sentiment analysis. This evaluation entails employing multiple translation tools or engaging multiple human translators to cross-reference translations, thereby facilitating the identification of potential inconsistencies or discrepancies. Additionally, techniques such as back-translation can be employed, whereby the translated text is retranslated back into the original language and compared to the initial text to discern any disparities. By undertaking rigorous quality assessment measures, the potential biases or errors introduced during the translation process can be effectively mitigated, enhancing the reliability and accuracy of sentiment analysis outcomes.

Social media platforms such as YouTube have sparked extensive debate and discussion about the recent war. As such, we believe that sentiment analysis of YouTube comments about the Israel-Hamas War can reveal important information about the general public’s perceptions and feelings about the conflict16. Moreover, social media’s explosive growth in the last decade has provided a vast amount of data for users to mine, providing insights into their thoughts and emotions17. Social media platforms provide valuable insights into public attitudes, particularly on war-related issues, aiding in conflict resolution efforts18. Despite their precision and time-consuming nature, machine-learning algorithms are the foundation of sentiment analysis16. As we enter the era of ‘data explosion,’ it is vital for organizations to optimize this excess yet valuable data and derive valuable insights to drive their business goals.

An inherent limitation in translating foreign language text for sentiment analysis revolves around the potential introduction of biases or errors stemming from the translation process44. Although machine translation tools are often highly accurate, they can generate translations that deviate from the fidelity of the original text and fail to capture the intricacies and subtleties of the source language. Similarly, human translators generally exhibit greater accuracy but are not immune to introducing biases or misunderstandings ChatGPT App during translation. For instance, certain cultures may predominantly employ indirect means to express negative emotions, whereas others may manifest a more direct approach. Consequently, if sentiment analysis algorithms or models fail to account for these cultural disparities, precisely identifying negative sentiments within the translated text becomes arduous. The outcomes of this experimentation hold significant implications for researchers and practitioners engaged in sentiment analysis tasks.

Two types of filters were successfully implemented to collect the required data. A HTML parser is used to parse the obtained data, which yielded 500 news stories with 700 sentences containing the keywords mentioned above. Nearly 6000 sentences not annotated with emotions were discarded from those 500 news articles. With its sentiment analysis tool, users can transform unstructured data into easily understandable categories and generate actionable insights for their business.

Recurrent neural networks (RNNs) and their variants Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bi-directional Long-Short Term Memory (Bi-LSTM), and Bi-directional Gated Recurrent Unit (Bi-GRU) architectures are robust at processing sequential data. They are commonly used for NLP applications as they—unlike RNNs—can combat vanishing and exploding gradients. Also, Convolution Neural Networks (CNNs) were efficiently applied for implicitly detecting features in NLP tasks. In the proposed work, different deep learning architectures composed of LSTM, GRU, Bi-LSTM, and Bi-GRU are used and compared for Arabic sentiment analysis performance improvement. The models are implemented and tested based on the character representation of opinion entries. You can foun additiona information about ai customer service and artificial intelligence and NLP. Moreover, deep hybrid models that combine multiple layers of CNN with LSTM, GRU, Bi-LSTM, and Bi-GRU are also tested.

What is sentiment analysis?

Next, the data is split into train and test sets, and different classifiers are implemented starting with Logistic Regression. A confusion matrix is used to determine and visualize the efficiency of algorithms. The confusion matrix of ChatGPT both sentiment analysis and offensive language identification is described in the below Figs. The class labels 0 denotes positive, 1 denotes negative, 2 denotes mixed feelings, and 3 denotes an unknown state in sentiment analysis.

The study also revealed that while most political and ideological frames were viewed unfavorably, economic frames were consistently viewed more favorably. The first (referred to as BERT-truncated) considered only the first 30% of the tokens resulting from the tokenization procedure of the input news article. We truncated or padded the token vector with zeros to get 510 elements and added the classification [CLS] and separation [SEP] tags. The resulting vector was fed into a pre-trained BERT encoder, which computed a 768-element encoding vector for each token.

• Research conducted on social media data often leverages other auxiliary features to aid detection, such as social behavioral features65,69, user’s profile70,71, or time features72,73.
• The site’s focus is on innovative solutions and covering in-depth technical content.
• However, sexual harassment is not limited to the online sphere but also occurs in various forms, including gender harassment, unwanted sexual attention, and sexual coercion in different settings such as workplaces, educational institutions, public places, and homes.
• Based on the results of textual entailment analysis, the study further investigates translation universals at the semantic level and collects evidence for the influence of the translation process on informational explicitness as well as the semantic structure.
• Also, many issues exist in TM approaches with short textual data within OSN platforms, like slang, data sparsity, spelling and grammatical errors, unstructured data, insufficient word co-occurrence information, and non-meaningful and noisy words.

In another context, the impact of morphological features on LSTM and CNN performance was tested by applying different preprocessing steps steps such as stop words removal, normalization, light stemming and root stemming41. It was reported that preprocessing steps that eliminate text noise and reduce distortions in the feature space affect the classification performance positively. Whilst, preprocessing actions that cause the loss of relevant morphological information as root stemming affected the performance. Also, in42, different settings of LSTM hyper-parameters as batch size and output length, was tested using a large dataset of book reviews.

• There have been very few research studies on Urdu SA, and it is still in its early stages of maturation compared to other resource-rich languages like English.
• Sentiment analysis lets you understand how your customers really feel about your brand, including their expectations, what they love, and their reasons for frequenting your business.
• Moreover, the average number of argument structures in Chinese sentences should be bigger than that in English sentences since they have a similar average number of semantic roles in a sentence.
• As you look at how users interact with your brand and the types of content they prefer, you can retool your brand messaging for greater impact.
• As technology and awareness grow, more people are using the internet for global communication, online shopping, sharing their experiences and thoughts, remote education, and correspondence on numerous aspects of life3,4,5.

The difference being that the root word is always a lexicographically correct word (present in the dictionary), but the root stem may not be so. Thus, root word, also known as the lemma, will always be present in the dictionary. The Porter stemmer is based on the algorithm developed by its inventor, Dr. Martin Porter.