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Social media has become an invaluable resource for gathering customer feedback in today’s digital era. As technology and the internet evolve, vast quantities of data are generated from numerous sources such as social blogs and websites. Recently, webpages and blogs have emerged as primary channels for obtaining real-time consumer feedback. However, the sheer volume of blogs on the cloud has led to overwhelming information in various forms, including attitudes, opinions, and reviews. For product reviews, it is possible to discern the reviewer’s perspective on specific product characteristics rather than the product as a whole. Nonetheless, sarcastic reviews pose a challenge in accurately determining the sentiment class of the review. This paper proposes a sarcastic review prediction-based sentiment analysis approach to address this issue. The proposed method comprises three stages: pre-processing, feature extraction, and prediction. Initially, reviews are collected and pre-processed using tokenization, stop word removal, and stemming. Following pre-processing, feature extraction is performed, wherein sentimental, punctuation, and TF-IDF features are extracted from each review. These extracted features are then fed into a Bidirectional Long Short-Term Memory (Bi-LSTM) network with an attention mechanism to predict whether a product review is sarcastic or non-sarcastic. The fusion attention mechanism assigns attentive weights to each word, enhancing the prediction accuracy. The performance of the proposed approach is evaluated using various metrics and compared against state-of-the-art techniques. The results demonstrate the effectiveness of the proposed method in accurately predicting sarcastic reviews.

Nowadays, all the records in various languages are accessible with their advanced structures. For simple recovery of these digitized records, these reports should be ordered into a class as indicated by their content. Text Categorization is an area of Text Mining which helps to overcome this challenge. Text Classification is a demonstration of allotting classes to records. This paper investigates Text Classification works done in foreign Languages, regional languages and a list of books’ content. Messages available in different languages force the difficulties of NLP approaches. This study shows that supervised ML algorithms such as Logistic regression, Naive Bayes classifier, $k$-Nearest-Neighbor classifier, Decision Tree and SVMs performed better for Text Classification tasks. The automated document classification technique is useful in our day-to-day life to find out the type of language and different department books based on their text content. We have been using different foreign and regional languages here to classify such as Tamil, Telugu, Kannada, Bengali, English, Spanish, French, Russian and German. Here, we utilize one versus all SVMs for multi-characterization with 3-crease Cross Validation in all cases and see that SVMs outperform different classifiers. This implementation is done by using hybrid classifiers and it depicts analyses with delicate edge straight SVMs as well as bit-based SVMs.

The text content of Aviation Safety Confidential Reports contains a large number of valuable information. Term frequency-inverse document frequency algorithm is commonly used in text analysis, but it does not take into account the sequential relationship of the words in the text and its role in semantic expression. According to the seven category labels of civil aviation unsafe incidents, aiming at solving the problems of TF-IDF algorithm, this paper improved TF-IDF algorithm based on co-occurrence network; established feature words extraction and words sequential relations for classified incidents. Aviation domain lexicon was used to improve the accuracy rate of classification. Feature words network model was designed for multi-documents unsafe incidents classification, and it was used in the experiment. Finally, the classification accuracy of improved algorithm was verified by the experiments.

Sentiment classification of financial news deals with the identification of positive and negative news so that they can be applied in decision support systems for stock trend predictions. This paper explores several types of feature spaces as different data spaces for sentiment classification of the news article. Experiments are conducted using $N$-gram models unigram, bigram and the combination of unigram and bigram as feature extraction with traditional feature weighting methods (binary, term frequency (TF), and term frequency-document frequency (TF-IDF)), while document frequency (DF) was used in order to generate feature spaces with different dimensions to evaluate $N$-gram models and traditional feature weighting methods. We performed some experiments to measure the classification accuracy of support vector machine (SVM) with two kernel methods of Linear and Gaussian radial basis function (RBF). We concluded that feature selection and feature weighting methods can have a substantial role in sentiment classification. Furthermore, the results showed that the proposed work which combined unigram and bigram along with TF-IDF feature weighting method and optimized RBF kernel SVM produced high classification accuracy in financial news classification.

A major challenge in document clustering is the extremely high dimensionality as well as the sparsity of the sample matrix. In this paper, we propose a new short-text oriented analysis approach to cluster short text automatically and extract the hot topics from each cluster. Different from the previous studies focused on long text, our analysis approach mainly focused on short-text cases. The approach consists of three stages: Firstly, generate feature vector for each sample so as to obtain the whole high-dimensional Vector Space Model; Secondly, use Singular Value Decomposition to achieve the dimensions reduction; Lastly, apply cosine similarity and k-means method to cluster samples on the low-dimensional matrix and extract the hot topics for each cluster. The experimental results show that our analysis approach can deal with the short-text samples and find out the hot topics efficiently and effectively.

Social media networking sites, such as Instagram, Facebook, and Twitter, have become an inextricable part of our everyday lives. These social media networks are useful for sharing news, images, and other information. The study of social media sentiment has recently received a lot of attention, especially in Arabic sentiment analysis. Social media sites are distinguished by unusual language that differs from the traditional format of the language. As a result, there is a necessity for efficient ways for analyzing the massive amount of new word variants that appear regularly in the digital world and online world. This study proposes a piecewise Stochastic Gradient Descent (SGD)-based model for sentiment classification. The TF-IDF-based term weighting scheme is employed for textual feature representation. For enhancing the model performance, stemming and partially ordered microword representation of tweets with varying look ahead distances is employed. Also, various n-gram models are considered for textual feature representation, which also improves the model performance. The proposed model is simulated and evaluated with the help of publicly available tweet corpus⁹ which is a balanced tweet corpus. The effectiveness of the proposed model is estimated using various performance evaluation metrics. According to the experimental observations, the proposed method accurately categorizes the testing and validation tweets set with an accuracy of up to 92.23%.

This paper intends to develop a novel framework for text document clustering with the aid of a new improved meta-heuristic algorithm. Initially, the features are selected from the text document by subjecting each word under Term Frequency-Inverse Document Frequency (TF-IDF) computation. Subsequently, centroid selection plays a vital role in cluster formation, which is done using a new Improved Lion Algorithm (LA) termed as Cross over probability-based LA model (CP-LA). As a novelty, this paper introduced a new inter and intracluster similarity model. Moreover, this centroid selection is made in such a way that the proposed adaptive weighted similarity should be minimal. Based on the characteristics of the document, the weights are automatically adapted with the similarity measure. The proposed adaptive weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents. Finally, the superiority of the proposed over other models is proved under different performance measures.

Genetics is the clinical review of congenital mutation, where the principal advantage of analyzing genetic mutation of humans is the exploration, analysis, interpretation and description of the genetic transmitted and inherited effect of several diseases such as cancer, diabetes and heart diseases. Cancer is the most troublesome and disordered affliction as the proportion of cancer sufferers is growing massively. Identification and discrimination of the mutations that impart to the enlargement of tumor from the unbiased mutations is difficult, as majority tumors of cancer are able to exercise genetic mutations. The genetic mutations are systematized and categorized to sort the cancer by way of medical observations and considering clinical studies. At the present time, genetic mutations are being annotated and these interpretations are being accomplished either manually or using the existing primary algorithms. Evaluation and classification of each and every individual genetic mutation was basically predicated on evidence from documented content built on medical literature. Consequently, as a means to build genetic mutations, basically, depending on the clinical evidences persists a challenging task. There exist various algorithms such as one hot encoding technique is used to derive features from genes and their variations, TF-IDF is used to extract features from the clinical text data. In order to increase the accuracy of the classification, machine learning algorithms such as support vector machine, logistic regression, Naive Bayes, etc., are experimented. A stacking model classifier has been developed to increase the accuracy. The proposed stacking model classifier has obtained the log loss 0.8436 and 0.8572 for cross-validation data set and test data set, respectively. By the experimentation, it has been proved that the proposed stacking model classifier outperforms the existing algorithms in terms of log loss. Basically, minimum log loss refers to the efficient model. Here the log loss has been reduced to less than 1 by using the proposed stacking model classifier. The performance of these algorithms can be gauged on the basis of the various measures like multi-class log loss.

Designing efficacious semantics for the dynamic interaction and searches has proven to be concretely challenging because of the dynamically of the semantic searches, method of browsing and visualization interfaces for high volume information. This has a direct impact on enhancing the capabilities of the web. To surmount the challenges of providing meaning to high volume unstructured datasets, Natural language processing techniques and implements have been proven to be propitious, however, the reactivity of these techniques should be studied and predicated on the objective of providing meaning to the unstructured data. This paper demonstrates the working of five NLP techniques namely, bag-of-words, TF-IDF, NER, LSA, and LDA. The experiment provides the kindred attribute accomplishment or the identification of the meaning of this unstructured data varies from one technique to another. However, NLP techniques can be efficient as they provide insights into the data and make it human-readable. This will in turn avail in building better human–machine intractable browsing and applications.

To determine the similarity measure of news articles, this paper proposes a hybrid approach based on the dictionary and research done by previous studies. We identify representative nouns from news articles, and combine the word similarity metric into the text metric, which helps the news provider to filter out similar articles to the subscriber. The experiments are conducted to examine a set of press releases and news articles, while the results demonstrate that our proposed method outperforms the TF-IDF-based approach.