SENTI2POP: Sentiment-Aware Topic Popularity Prediction on Social Media

An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

The advancement of internet technology has facilitated the spread of information, including false information or fake news. The dissemination of hoaxes on social media, such as Twitter, can cause confusion and negatively impact society. This study aims to implement a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) for hoax detection. The dataset used consists of English tweets containing both real and fake news, collected between 2020 and 2022, as provided by the TruthSeeker dataset. The model utilizes an embedding layer with word2vec, a Conv1D layer, and a BiLSTM layer to effectively capture temporal and spatial patterns in text data. Additionally, experiments were conducted by varying the number of BiLSTM units and CNN filters to analyze their impact on model performance. After conducting parameter experiments, the best results were achieved using a Conv1D layer with 64 filters and a BiLSTM layer with 64 neurons/units. The evaluation results on the test data indicate an accuracy of 96.14%, a precision of 96%, a recall of 96.25%, and an F1-score of 96%. These results demonstrate the model's high capability in accurately detecting hoaxes, which is significant for combating misinformation on social media. With its strong performance, the model has potential applications in real-time content moderation systems, early hoax detection tools, and digital literacy platforms to help reduce the spread of false information.

Aspect based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, whose main goal is to identify the sentiment polarity of an aspect in a sentence. A sentence may contain many different aspects, each of which may have different sentiment polarities. Based on the current researches in this area, ABSA can be divided into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). In the past, more commonly used method is to adopt the time serial algorithm such as Long Short-Term Memory (LSTM) or Recurrent Neural Network (RNN), which usually needs more training time and has complex structures. Moreover, many previous models lack the abilities to effectively learn the internal structure features of sentences. For ABSA, sometimes the sentence structure may significantly affect the final classification results. However, we found the excellent performance of self-attention algorithm and gating mechanism in some other related researches. Therefore, to solve the problems above, we build a new model based on gating mechanism, combined with convolutional neural networks (CNN) and self-attention mechanism. First, we use self-attention to extract the structural feature of the input, and integrate it with the features of the original sentence extracted by CNN. On such basis, we further combine the aspect-category or aspect-term of the input sentence to form the final sentiment feature. Experiments on SemEval datasets show the performance of our models and the effectiveness of the model is proved.

In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction.

Sentiment Analysis (SA) is a computational study that examines people’s opinions, attitudes, and opinions based on their written text. Keralites’ mother tongue, Malayalam, is the most often used language to express themselves on Twitter. As there is no automatic Sentiment Analyzer in Malayalam, the SA of Twitter messages is necessary. In this research work, a Malayalam sentiment analysis model is introduced. The input raw data in the form of reviews are fed for the pre-processing stage. The pre-processing module includes sentence tokenization via Sandi Splitting and parts of speech (POS) tagging. Subsequently, Bag of Word, the Proposed weightage-based Term Frequency-Inverse Document Frequency, Unigram with the dictionary and Unigram with dictionary including negation words have been considered for feature vector formation. Finally, the review classification is undergone via the proposed hybrid classifier, which is constructed by hybridizing the Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), respectively. To enhance the classification performance, the weight of the CNN classifier will be fine-tuned via an Improved Bumble bee mating Optimization (IBBMO), which is an advanced version of standard BBMO. The performance of the proposed work is compared over other conventional models in terms of positive, negative, convergence, and other measures. The accuracy of the proposed method is 95.436, which is much better than the existing works like DBN[Formula: see text]86.25, NN[Formula: see text]87.080, SVM[Formula: see text]87.234, WOA+Hybrid Classifier[Formula: see text]88.440, LA+Hybrid Classifier[Formula: see text]89.35285, CSO+Hybrid Classifier[Formula: see text]92.021, BBMO+Hybrid Classifier[Formula: see text]93.4248, and ML techniques[Formula: see text]94.014, at the 90th training percentage.

The rise of social media has provided an unprecedented opportunity to analyze public opinion at scale. Sentiment analysis, a key technique in natural language processing (NLP), enables the automatic classification of opinions expressed in textual data. This study explores the effectiveness of sentiment analysis using the Sentiment140 dataset, a collection of tweets labeled as positive, negative, or neutral. We compare machine learning and deep learning models, including logistic regression, support vector machines (SVM), convolutional neural networks (CNN), and long short-term memory (LSTM) networks. Our results highlight the comparative strengths of each model in classifying sentiments with varying degrees of accuracy. This research contributes to the ongoing development of AI-powered sentiment analysis by evaluating model performance on real-world social media data.

Sentiment analysis or opinion mining used to capture the community's attitude who have experienced the specific service/product. Sentiment analysis usually concentrates to classify the opinion of whole document or sentence. However, in most comments, users often express their opinions on different aspects of the mentioned entity rather than express general sentiments on entire document. In this case, using aspect-based sentiment analysis (ABSA) is a solution. ABSA emphases on extracting and synthesizing sentiments on particular aspects of entities in opinion text. The previous studies have difficulty working with aspect extraction and sentiment polarity classification in multiple domains of review. We offer an innovative deep learning approach with the integrated construction of bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for multidomain ABSA in this article. Our system finished the following tasks: domain classification, aspect extraction and opinion determination of aspect in the document. Besides applying GloVe word embedding for input sentences from mixed Laptop_Restaurant domain of the SemEval 2016 dataset, we also use the additional layer of POS to pick out the word morphological attributes before feeding to the CNN_BiLSTM architecture to enhance the flexibility and precision of our suggested model. Through experiment, we found that our proposed model has performed the above mentioned tasks of domain classification, aspect and sentiment extraction concurrently on a mixed domain dataset and achieved the positive results compared to previous models that were performed only on separated domain dataset.

Speech spectrograms exhibit strong contextual dependencies along both time and frequency dimensions. In this paper, a novel composite model integrating a long short-term memory (LSTM) and convolutional neural network (CNN) to exploit temporal and spectral contextual speech information, respectively, is proposed for speech separation. LSTM and CNN operate in a parallel fashion to speed up the process and independently extract a complementary set of speech features. A fully-connected network then maps these features to the real and imaginary components of a ratio mask to enhance the magnitude and phase of the corrupted speech simultaneously. In the CNN path, a new delicately designed CNN with frequency dilated one-dimension (1D) convolutional layers is employed to expand the receptive field of CNN kernels without increasing the complexity. Furthermore, this CNN benefits from residual learning and skip connections to facilitate training and accelerate convergence. In spite of different neural networks included in the composite model, the proposed separation system not only has a low computational complexity, but also significantly outperforms some other deep learning-based methods.

Text classification is a classic task in the field of natural language processing. however, the existing methods of text classification tasks still need to be improved because of the complex abstraction of text semantic information and the strong relecvance of context. In this paper, we combine the advantages of two traditional neural network model, Long Short-Term Memory(LSTM) and Convolutional Neural Network(CNN). LSTM can effectively preserve the characteristics of historical information in long text sequences, and extract local features of text by using the structure of CNN. We proposes a hybrid model of LSTM and CNN, construct CNN model on the top of LSTM, the text feature vector output from LSTM is further extracted by CNN structure. The performance of the hybrid model is compared with that of other models in the experiment. The experimental results show that the hybrid model can effectively improve the accuracy of text classification.

Human identification using camera-based surveillance systems is a challenging research topic, especially in cases where the human face is not visible to cameras and/or when humans captured on cameras have no clear visual identity owing to environments with low-illumination. With the development of deep learning algorithms, studies that are based on the human gait using convolutional neural networks (CNNs) and long short-term memory (LSTM) have achieved promising performance for human identification. However, CNN and LSTM-based methods have the limitation of having higher loss of temporal and spatial information, respectively. In our approach, we use shallow CNN stacked with LSTM and deep CNN followed by score fusion to capture more spatial and temporal features. In addition, there have been a few studies regarding gait-based human identification based on the front and back view images of humans captured in low-illumination environments. This makes it difficult to extract conventional features, such as skeleton joints, cycle, cadence, and the lengths of walking strides. To overcome these problems, we designed our method considering the front and back view images captured in both high- and low-illumination environments. The experimental results obtained using a self-collected database and the open database of the institute of automation Chinese academy of sciences gait dataset C show that the proposed method outperforms previous methods.

Yoga is an ancient art form originated in India that makes one fit physically and also provides mental peace/relaxation. Yoga has become popular all over the world because of the increase in stress in modern lifestyle. A model has has been developed which detects various yoga poses. An open-source dataset of 88 videos consisting of 6 asanas performed by 15 different volunteers have been used. Various approaches have been studied that are available for pose detection and the most suitable approach has been chosen to meet the requirements. A combo of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN), called Long-Term Recurrent Convolutional Network (LRCN) has been put to use. LSTM is used to make temporal prediction, whereas CNN is used to extract information from each frame. The proposed model differs from existing models as it does not use open pose or pose net for keypoint detection. Using the proposed model, accuracy of 81 achieved.

Long-term load forecasting is required for power system planning, long-term investments, and expansion policies. In this paper, an ensemble of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) is proposed that minimizes the forecasting error for the long-term. The features are extracted from the decomposition of load series using Variational Mode Decomposition (VMD) into various timescale subseries' and are combined with the exogenous variables like temperature and rain. This data is then used to train a Fully Connected Neural Network (FCNN) that produces the final output. This ensemble is expected to provide an accurate forecast as local variations in the series are captured by CNN and long-term dependencies by LSTM, while the VMD decomposes the raw data into low and high-frequency subsequences. The efficacy of the methodology is assessed on the actual load data of Madhya Pradesh (MP) State, India. It is observed that the proposed method has higher forecasting accuracy.

As social media and online social networking sites have emerged as essential channels for everyday social interactions, researchers are increasingly turning to conversational social media as a valuable alternative source of insights into public sentiment and behavior during times of crisis. In a similar vein, our study investigates the utility of using conversational social media data for analyzing public sentiment related to the COVID-19 pandemic. Our research question is, to what extent can machine learning models based on conversational social media data help gauge public sentiment during a pandemic? To evaluate the performance of various machine learning algorithms in sentiment analysis tasks, we used a Twitter dataset and compared their effectiveness using eight different models (i.e., Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Extreme Gradient Boosting (XGBoost), Logistic Regression, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT)). The results show that among the eight models, the top three models are BERT, LSTM, and SVM, achieving overall accuracies of 87.44%, 81.94%, and 80.02%, respectively. Employing sentiment analysis models on pandemic-related social media posts allows us to gain insights into of human emotions and sentiments during public health crises like a pandemic.

Remaining useful life (RUL) prediction is an valuable research task for predictive health management of aero engines, which is crucial to enhance the safety, dependability and economy of the motor. Accurate prediction of remaining engine service life is an important means for the effectively monitoring of engine operating conditions. The forecasting accuracy of turbofan RUL is inadequate with the traditional single-parameter, single-stage mode. To boost the precision of aero-engine RUL anticipation, a novel mode is recommended, based on a parallel convolutional neural network (CNN) with a long and short-term memory (LSTM) neural network and a dual attention mechanism, named PCLD. The degradation information directly from time series sensor data. The advantages of CNN networks and LSTM networks in feature mining and time series processing, respectively, are employed to process time series data, which is conducive to preventing the loss of important element in the data. At last, the experimental results on the aero-engine performance recession dataset C-MAPSS demonstrate that the method outperforms the currently popular models with better robustness and higher prediction accuracy.

<p>Geomagnetic indices can have a central role in the mitigation of ground effects due to space weather events, for instance when their reliable forecasting will be achieved. To this purpose, machine learning techniques represent a powerful tool. Here, we use two conceptually different neural networks to forecast the SYM-H index: the long short-term memory (LSTM) and the convolutional neural network (CNN). We build two models and train both of them using two different sets of input parameters including interplanetary magnetic field components and magnitude and differing for the presence or not of previous SYM-H values. Both models are trained, validated, and tested on a total of 42 geomagnetic storms among the most intense that occurred between 1998 and 2018. Results show that both models are able to well forecast SYM-H index 1 hour in advance. The main difference between the two stands in the better performance of the one based on LSTM when SYM-H index is included in the input parameters and, contrarily, in the better performance of the one based on CNN for predictions based only on interplanetary magnetic field data.</p>