Hindi and Kannada Twitter Sentiment Analysis Using Machine Learning Algorithm

In this study, we explore the potential of machine learning (ML) models after different text representation methods on the balanced IMDB dataset, which is widely regarded as a gold standard in sentiment analysis, one of the Natural Language processing (NLP) tasks. On the open source IMDB movie reviews dataset, we first undertake data cleaning and text representation with data preprocessing steps. Then, we apply sentiment classification using different ML models. In order to evaluate the models, we used precision (P), recall (R), F1-score (F1), and area under curve (AUC), as well as receiver operating characteristic (ROC). It is worth noting that text feature extraction with Bidirectional Encoder Representations from Transformers (BERT) provided the highest performance in all models, with the SVM model offering particularly promising results. In this model, we observed the following results: ACC 0.9033, F1 0.9308, R 0.9015, R 0.9015, P 0.9072, AUC 0.9638, and ROC 0.96. These findings suggest that NLP techniques and, in particular, machine learning models that employ BERT may offer high levels of accuracy and reliability in text classification problems. It would be beneficial for future studies to validate these findings using BERT on different NLP tasks. This would help to evaluate the effectiveness and applicability of the models in practice.

Online networking has gotten more consideration these days. Public and private sentiment about a wide assortment of subjects are communicated and spread consistently by means of various internet based life. Twitter is one of the internet based life plateform that is picking up notoriety. Twitter offers associations a quick and compelling approach to break down clients' viewpoints toward the basic to achievement in the commercial center. Building up a program for sentiment analysis is a way to deal with is utilized to computationally quantify client’s recognitions. Twitter sentiment analysis is the method for recognizing feelings and polarity in tweets. The primary computational strides right now deciding the extremity or notion of the tweet and afterward ordering them into the positive tweet or negative tweet or neutral tweet. The essential issue with Twitter sentiment analysis is the distinguishing proof of the most appropriate sentiment classifier that can accurately group the tweets. For the most part, base classification methods like Naive Bayes classifier, Random Forest classifier, SVMs and so on are being utilized. The objective of this paper is to anticipate feelings for the given Twitter post utilizing Python. Sentiment analysis can predict a wide range of feelings joined to the content, however right now 3 significant were considered: positive, negative and neutral. The training dataset is (a little more than 5900 models) and the information inside it is exceptionally skewed, which incredibly affected on the trouble of building great classifier. Subsequent to making a great deal of custom highlights, using both bag-of-words and word2vec portrayals and applying distinctive classification techniques, we accomplish great precision and accuracy for 3 emotions.

Sentiment analysis is an important task in natural language processing, which has promises great value to areas of interests such as business, politics and other fields. The prevalence of the internet has caused people to prefer expressing their opinion and sentiment on the Internet via methods such as tweeting on social media and commenting on products. However, the discourse of users on social media are usually short either due to limitations on post size or time limitations of the users. What emerges from these features is that the grammar used in these posts and the word meanings are flexible, which makes sentiment analysis difficult. Therefore, sentiment analysis is an important yet challenging task. In this paper, we propose a context-based regularization classification method for short text sentiment analysis. Specifically, we use contextual knowledge obtained from the data to improve performance of the sentiment classification. In this paper, the contextual knowledge includes two parts: word-sentiment knowledge and word-similarity knowledge. Moreover, we propose methods to calculate the sentiment of the words and the similarity between words on semantic level. Specifically, on the one side, we use a TRSR method based on the TextRank algorithm to rank words in each sentiment sample to determine the sentiment polarity of each word. On the other, we calculate the similarity between words according to word-embedding. In this way, we can determine the similarity between words and the sentiment polarity of a word. We then incorporate the contextual knowledge as a regularization into a supervised classification framework, which then converts into an optimization problem to train a more accurate model. Experiments on both Chinese and English datasets outperform than other baseline approaches, which demonstrates our method to be stable and effective.

Towards a Novel Weakly Supervised Joint Approach of Named Entity Recognition and Normalization for Noisy Text

Towards a Novel Weakly Supervised Joint Approach of Named Entity Recognition and Normalization for Noisy Text

Natural Language Processing (NLP) has emerged as a fundamental element of contemporary artificial intelligence, enabling systems that vary from chatbots to sentiment analyzers [1].Transforming unstructured text into organized, clean input is essential for attaining optimal performance in NLP tasks [2]. Regular expressions (RegEx) provide a rule-based, interpretable, and quick method for text tokenization, cleansing, and pattern extraction [3]. However, despite their effectiveness, RegEx methods are constrained by their failure to manage linguistic ambiguity and the variability present in natural language [4]. In comparison, machine learning (ML) models, particularly deep learning structures like BERT and LSTMs, offer strong semantic comprehension but necessitate vast amounts of training data and computational resources [5]. This document presents a hybrid framework that combines the rapid, rule based accuracy of RegEx with the profound semantic abilities of ML models to improve the overall performance of NLP systems [6]. We present a thorough literature review, elaborate algorithmdesigns complete with pseudocode, examples of code implementations, and an enhanced system architecture that remedies the limitations of existing methods [7]. The suggested method is assessed on benchmark tasks including sentiment analysis, named entity recognition (NER), and spam detection, demonstrating considerable advancements in both accuracy and efficiency [8].

Sentiment analysis is a relevant task in natural language processing, which is often conducted on Internet texts to analyze reviews of products, services, posts, and comments on social media. Unlike most studies, our work focuses on literary texts in the Russian language written during the emotionally charged period of the early 20th century, when a change in the Russian political system and a fundamental shift in society's way of life occurred as a result of socio-economic upheavals such as wars and revolutions. It is assumed that literary texts from this period should also be rich in emotional vocabulary. The main goal of the research described in this article is to study the correlation between the results of sentiment analysis, performed on the material of Russian short stories using several automatic methods, and the average expert evaluation of the emotions that these same texts evoke in modern readers. The results of the study contribute to understanding the evaluative component of literary works vocabulary and can also be used to build recommendation systems aimed at selecting literary texts for readers.

Objective: To make an extensive analysis of sentiment within the discourse surrounding COVID-19 vaccines on Twitter, employing Natural Language Processing (NLP) methodologies. Method: The research methodology encompasses data collection via the Twitter API, followed by sentiment analysis facilitated by the TextBlob library. Pre-processing stages are integrated to cleanse and standardize the Twitter data. Subsequently, sentiment analysis categorizes tweets into positive, negative, and neutral sentiments based on polarity scores. Findings: The findings, grounded in a dataset spanning from March 1, 2022, to April 30, 2022, comprising 61,934 tweets, unveil that 45.0% of tweets conveyed positive sentiment, 17.3% exhibited negativity, and 37.7% maintained neutrality. Moreover, an exploration of tweet subjectivity revealed that 70.1% of the content expressed subjectivity, while 29.9% conveyed objectivity. The research is augmented with visual representations, including word clouds and subjectivity-polarity graphs, that offer a more intuitive understanding of sentiment trends. Novelty: This study contributes to the expanding landscape of sentiment analysis and its application within the context of public health crises, empowering stakeholders with valuable knowledge to enhance vaccine acceptance and effectiveness. The tool used “Tweet Downloader” in data collection makes this study different from other reviewed studies. Keywords: COVID-19 vaccine, Twitter sentiment analysis, Public perception, Natural Language Processing (NLP), Social media data

ChatGPT OpenAI, a large-language chatbot model, has gained a lot of attention due to its popularity and impressive performance in many natural language processing tasks. ChatGPT produces superior answers to a wide range of real-world human questions and generates human-like text. The new OpenAI ChatGPT technology may have some strengths and weaknesses at this early stage. Users have reported early opinions about the ChatGPT features, and their feedback is essential to recognize and fix its shortcomings and issues. This study uses the ChatGPT tweets Arabic dataset to automatically find user opinions and sentiments about ChatGPT technology. The dataset is preprocessed and labeled using the TextBlob Arabic Python library into positive, negative, and neutral tweets. Despite extensive works for the English language, languages like Arabic are less studied regarding tweet analysis. Existing literature about Arabic tweet sentiment analysis has mainly focused on machine learning and deep learning models. We collected a total of 27,780 unstructured tweets from Twitter using the Tweepy SNscrape Python library using various hash-tags such as # Chat-GPT, #OpenAI, #Chatbot, Chat-GPT3, and so on. To enhance the model’s performance and reduce computational complexity, unstructured tweets are converted into structured and normalized forms. Tweets contain missing values, URL and HTML tags, stop words, punctuation, diacritics, elongations, and numeric values that have no impact on the model performance; hence, these increase the computational cost. So, these steps are removed with the help of Python preprocessing libraries to enhance text quality and consistency. This study adopts Transformer-based models such as RoBERTa, XLNet, and DistilBERT that automatically classify the tweets. Additionally, a hybrid transformer-based model is proposed to obtain better results. The proposed hybrid model is developed by combining the hidden outputs of the RoBERTA and BERT models using a concatenation layer, then adding dense layers with “Relu” activation employed as a hidden layer to create non-linearity and a “softmax” activation function for multiclass classification. They differ from existing state-of-the-art models due to the enhanced capabilities of both models in text classification. Hybrid models combine the different models to make accurate predictions and reduce bias and enhanced the overall results, while state-of-the-art models are incapable of making accurate predictions. Experiments show that the proposed hybrid model achieves 96.02% accuracy, 100% precision on negative tweets, and 99% recall for neutral tweets. The performance of the proposed model is far better than existing state-of-the-art models.

The advances in deep learning have led to great achievements in many Natural Language Processing (NLP) tasks. With the nature of language, i.e., sequential data, most NLP tasks can be framed into the sequence learning framework, such as text generation. As one of the most important foundations for modern NLP techniques, autoregressive generation models have achieved dominant performance in a great deal of NLP tasks. Therefore, this thesis emphasizes improving the autoregressive generation model for different NLP tasks. While many tasks can naturally fit into the sequence learning framework, some of them, e.g., building discourse parsing tree, require sophisticated designs to fit into neural models. Therefore, this thesis firstly emphasizes a novel unified framework for discourse parsing, which builds a discourse tree in a top-down depth-first manner, and it frames the task as an autoregressive generation task with the goal of each step being the prediction of the node position given a piece of text. The proposed approach is proven effective with extensive empirical experiments. In addition, I extend the above framework by proposing a hierarchical decoder, which leverages the information from parents and siblings of the nodes that are currently processed. The proposed decoder utilizes the nature of the tree structure and further improves the experiment performance on both discourse parsing and dependency parsing tasks. On the other hand, the de facto strategies, i.e., cross entropy loss and teacher forcing, for training the autoregressive generation models have been shown problematic in certain aspects. For example, cross entropy loss, which is one of the widely leveraged training objective functions, often leads to text degeneration in text generation, and teacher forcing suffers from the exposure bias problem, where there exists a mismatch between the training and testing setup. For text degeneration, I introduce a class of diminishing attentions, which enforces the submodularity of the coverage calculated by cross attention in the sequence-to-sequence model. The proposed diminishing attentions achieve notable improvement on several neural text generation tasks, including text summarization, machine translation, and image paragraph generation. Further, I propose a novel training objective, ScaleGrad, to replace cross entropy, which significantly reduces the degeneration problem in different text generation tasks. In fact, ScaleGrad can be extended to problems beyond text degeneration. It provides wide flexibility to inject different inductive biases into the text generation model by directly modifying the gradient information in the output layer. Next, for the exposure bias problem, this thesis introduces a novel type of scheduled sampling based on training accuracy, which requires only minimal hyper-parameter tuning compared to existing scheduled sampling methods. Additionally, a novel imitation loss is proposed to further enforce the model’s generative behavior to match the teacher-forced behavior. Moreover, this thesis demonstrates that reducing exposure bias can improve the robustness of language models against repetition and toxic errors.

Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.

Text extraction is an important part of natural language processing (NLP) tasks. Most NLP tasks like text classification, machine translation, text-to-speech, text-based language identification, text summarization, and named-entity recognition involve the use of textual data. Such data is limited for low-resourced languages making it difficult to experiment advanced NLP techniques on these languages. This paper presents a Python-based toolkit for text analysis and text extraction from different types of images, documents, and audio files. The toolkit is built as a library that has functions that can be imported and utilized for text extraction.

Sentiment analysis, also known as opinion mining, is an important task in natural language processing and data mining. It involves extracting and analyzing subjective information from textual data to determine the sentiment or opinion expressed by the author. With the advancement of technology and the widespread use of social media and online review platforms, it is increasingly important to understand users' opinions and sentiments regarding a particular product, service or issue. The purpose of this research is to present a comprehensive literature review on sentiment analysis techniques. This research utilizes the systematic literature review method. This method involves systematic steps in searching, evaluating, and analyzing relevant literature in the field of sentiment analysis. The literature search was conducted through scientific databases and other reliable sources. Relevant articles were then selected based on pre-determined inclusion and exclusion criteria. The data from the selected articles were then comprehensively analyzed to identify the sentiment analysis techniques used and the key findings in the research. The results show that there are various techniques and approaches that have been developed and tested in sentiment analysis, some of the commonly used techniques include rule-based methods, classification-based methods, and machine learning-based methods.

The development of technology in the current era is very rapid, this is indicated by the many social media that have sprung up. One popular social media is Twitter. Twitter initially became a forum for social media users as a place to preach activities, discuss, and share stories between users. However, now Twitter is even a place for complaints for customers of a company, one of which is PT Telkom Indonesia. Some customers prefer not to contact the call centre that has been provided by the company to be contacted if there is a problem, but prefer to complain via Twitter. According to data taken during a certain period, 3324 tweets were obtained, which included the keywords indihome, myindihome, useetv, and wifi.id. The tweets data that has been collected, if processed properly, will be valuable information for the company. For example, as a reference to assess brand image, customer feedback, and marketing opportunities. This study classifies tweets where the keywords indihome, myindihome, useetv, and wifi.di. Furthermore, several data preprocessing techniques were carried out, sentiment analysis, and visualization in the form of histograms, pie charts, and word clouds. From 3324 tweets that have been analyzed, it is found that there are 34.4% positive tweets, 16.1% negative tweets, and 49.6% neutral tweets.

Long Short-Term Memory (LSTM) networks are unique to exercise data in its memory cell with long-term memory as Natural Language Processing (NLP) tasks have inklings of intensive time and computational power due to their complex structures like magnitude language model Transformer required to pre-train and learn billions of data performing different NLP tasks. In this paper, a dynamic chaotic model is proposed for the objective of transforming neurons states in network with neural dynamic characteristics by restructuring LSTM as Chaotic Neural Oscillatory-Long-Short Term Memory (CNO-LSTM), where neurons in LSTM memory cells are weighed in substitutes by oscillatory neurons to speed up computational training of language model and improve text classification accuracy for real-world applications. From the implementation perspective, five popular datasets of general text classification including binary, multi classification and multi-label classification are used to compare with mainstream baseline models on NLP tasks. Results showed that the performance of CNO-LSTM, a simplified model structure and oscillatory neurons state in exercising different types of text classification tasks are above baseline models in terms of evaluation index such as Accuracy, Precision, Recall and F1. The main contributions are time reduction and improved accuracy. It achieved approximately 46.76% of the highest reduction training time and 2.55% accuracy compared with vanilla LSTM model. Further, it achieved approximately 35.86% in time reduction compared with attention model without oscillatory indicating that the model restructure has reduced GPU dependency to improve training accuracy.