Long Short Term Memory (LSTM) based Deep Learning for Sentiment Analysis of English and Spanish Data

Saha, Baidya Nath; Senapati, Apurbalal

doi:10.1109/compe49325.2020.9200054

Cited by 16 publications

(6 citation statements)

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“…Given that each review of a product or service has a unique language, a single model may not be sufficient to accurately categorize the other data. These findings show that sentiment analysis is very context-and subject-dependent [3].…”

Section: Lstm For Sentiment Analysismentioning

confidence: 69%

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DocOps: LSTM-Based Text Sentiment Classification for Evaluating Patient Reviews and Doctor Selection

2023

IRJMETS

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The growing use of medical social media platforms has made it easier for people to share their opinions and experiences with healthcare providers. These subjective writings offer insightful information that may be used by patients and hospitals to make decisions. Using comment data from medical social media, we present a sentiment classification approach based on Long Short-Term Memory (LSTM) neural networks to assess the doctor-patient connection. Doctor feedback from various departments, sorting search results on rating, department of specialization, and so on is proposed. This study demonstrates the possibility of using sentiment analysis and medical social media to strengthen doctor-patient relationships and facilitate hospital choice.

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Section: Lstm For Sentiment Analysismentioning

confidence: 69%

“…For every data set separately, an LSTM-based RNN model was created, and the results were good [3]. LSTM with a hidden layer, a dense layer containing a neuron, and a sigmoid activation function are the embeddings.…”

Section: Lstm For Sentiment Analysismentioning

confidence: 99%

DocOps: LSTM-Based Text Sentiment Classification for Evaluating Patient Reviews and Doctor Selection

2023

IRJMETS

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“…LSTM networks, a specialized form of recurrent neural networks (RNNs), were introduced in 1997 [11] and are designed to emulate the learning processes of the human brain by retaining memory of previous inputs and discerning pivotal elements from non-essential ones [12]. LSTMs have found applications in a myriad of fields, including dynamic system modeling [13], image processing, speech recognition, sentiment analysis [14], and time series prediction [15]. An archetypal LSTM network, as depicted in Figure 1 [16], is composed of three distinct gates: the input gate, forget gate, and output gate.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Comparative analysis of ARIMA and LSTM for predicting fluctuating time series data

Taslim,

Murwantara

2024

Bulletin EEI

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The investigation of time series data forecasting is a critical topic within the realms of economics and business. The autoregressive integrated moving average (ARIMA) model has been prevalently utilized, notwithstanding its limitations, which include the necessity for a substantial quantity of data points and the presumption of data linearity. However, with recent developments, the long short-term memory (LSTM) network has emerged as a promising alternative, potentially overcoming these limitations. The objective of this study is to determine an effective approach for managing time series data characterized by volatility and missing values. Evaluation was conducted using RMSE for accuracy assessment, and the execution time measured using the Python Timeit library. The findings indicates that in a dataset comprising 60 data points, the LSTM model (RMSE 0.037618) surpasses the ARIMA model (RMSE 0.062667) in terms of accuracy. However, this trend reverses in a larger dataset of 228 data points, where the ARIMA model demonstrates superior accuracy (RMSE 0.006949) compared to the LSTM model (RMSE 0.036025). In scenarios with missing data, the LSTM model consistently outperforms the ARIMA model, although the accuracy of both models diminishes with an increase in the number of missing values. The ARIMA model significantly outpaces the LSTM model.

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“…The Tanh activation function, which maps the data between -1 and 1, receives the two values in the second section. The input gate's output, which modifies the cell state determined by multiplying the outputs of the Tanh and Sigma functions [43,44].…”

Section: ) Lstmmentioning

confidence: 99%

Strengthening Sentence Similarity Identification Through OpenAI Embeddings and Deep Learning

Korade,

Salunke,

Bhosle

et al. 2024

IJACSA

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Discovering similarity between sentences can be beneficial to a variety of systems, including chatbots for customer support, educational platforms, e-commerce customer inquiries, and community forums or question-answering systems. One of the primary issues that online question-answering platforms and customer service chatbots have is the large number of duplicate inquiries that are placed on the platform. In addition to cluttering up the platform, these repetitive queries degrade the content's quality and make it harder for visitors to locate pertinent information. Therefore, it is necessary to automatically detect sentence similarity in order to improve the user experience and quickly match user expectations. The present study makes use of the Quora dataset to construct a framework for similarity discovery in sentence pairs. As part of our research, we have built additional attributes based on textual data for improving the accuracy of similarity prediction. The study investigates several vectorization methods and their influence on accuracy. To convert preprocess text input to a numerical vector, we implemented Word2Vec, FastText, Term Frequency-Inverse Document Frequency (TF-IDF), CountVectorizer (CV), and OpenAI embedding. In order to judge sentence similarity, the embedding offered by several approaches was used with various models, including cosine similarity, Random Forest (RF), AdaBoost, XGBoost, LSTM, and CNN. The result demonstrates that all algorithms trained on OpenAI embedding yield excellent outcomes. The OpenAI-created embedding offers excellent information to models trained on it and has significant potential for capturing sentence similarity.

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Long Short Term Memory (LSTM) based Deep Learning for Sentiment Analysis of English and Spanish Data

Cited by 16 publications

References 0 publications

DocOps: LSTM-Based Text Sentiment Classification for Evaluating Patient Reviews and Doctor Selection

DocOps: LSTM-Based Text Sentiment Classification for Evaluating Patient Reviews and Doctor Selection

Comparative analysis of ARIMA and LSTM for predicting fluctuating time series data

Strengthening Sentence Similarity Identification Through OpenAI Embeddings and Deep Learning

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