Multi-Label Classification of Microblogging Texts Using Convolution Neural Network

Parwez, Md. Aslam; Abulaish, Muhammad; Jahiruddin,

doi:10.1109/access.2019.2919494

Cited by 44 publications

(17 citation statements)

References 16 publications

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“…They used Naïve Bayes for text classification, while we implemented a neural network. Neural networks have been shown to perform better than traditional machine learning mod-els 26,27 for text classification, in addition to other tasks like image classification. Rather than relying on the topics provided by Press Ganey, Doing-Harris et al 15 performed automatic topic modeling.…”

Section: Discussionmentioning

confidence: 99%

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback

Nawab¹,

Ramsey²,

Schreiber

2020

Appl Clin Inform

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Background Due to reimbursement tied in part to patients' perception of their care, hospitals continue to stress obtaining patient feedback and understanding it to plan interventions to improve patients' experience. We demonstrate the use of natural language processing (NLP) to extract meaningful information from patient feedback obtained through Press Ganey surveys. Methods The first step was to standardize textual data programmatically using NLP libraries. This included correcting spelling mistakes, converting text to lowercase, and removing words that most likely did not carry useful information. Next, we converted numeric data pertaining to each category based on sentiment and care aspect into charts. We selected care aspect categories where there were more negative comments for more in-depth study. Using NLP, we made tables of most frequently appearing words, adjectives, and bigrams. Comments with frequent words/combinations underwent further study manually to understand factors contributing to negative patient feedback. We then used the positive and negative comments as the training dataset for a neural network to perform sentiment analysis on sentences obtained by splitting mixed reviews. Results We found that most of the comments were about doctors and nurses, confirming the important role patients ascribed to these two in patient care. “Room,” “discharge” and “tests and treatments” were the three categories that had more negative than positive comments. We then tabulated commonly appearing words, adjectives, and two-word combinations. We found that climate control, housekeeping and noise levels in the room, time delays in discharge paperwork, conflicting information about discharge plan, frequent blood draws, and needle sticks were major contributors to negative patient feedback. None of this information was available from numeric data alone. Conclusion NLP is an effective tool to gain insight from raw textual patient feedback to extract meaningful information, making it a powerful tool in processing large amounts of patient feedback efficiently.

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Section: Discussionmentioning

confidence: 99%

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback

Nawab¹,

Ramsey²,

Schreiber

2020

Appl Clin Inform

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“…Softmax function is the suitable choice for multiclass classification (28) . Softmax considered useful because it converts the scores to a normalized probability distribution.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Artificial Neural Networks Based Integrated Crop Recommendation System Using Soil and Climatic Parameters

Madhuri¹,

Indiramma²

2021

IJST

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Objective : To develop crop recommendation system depending on location specific soil and climatic conditions. Method: The study introduces a novel recommendation system which uses Artificial Neural Networks (ANN) for recommending the suitable crop. The crops are recommended based on (a) Soil properties (b) Crop characteristics (c) Climate parameters. The crops namely maize, Finger millet, Rice and sugarcane is considered for the study. Depending on degree of relationship and limitations of the factors considered, following suitability classes are established: (a) Highly suitable: S1 (b) Moderately suitable: S2 (c) Marginally suitable: S3 (d) not suitable. The system uses the climate data from Meteorological survey of India and the soil data of Hadonahalli and Durgenahalli of Doddaballapur (dist.), Karnataka, India. The user interface developed takes the location specific soil properties as real time input and recommends the suitable crop considering the input and climate parameters.Findings: For the measurement of accuracy the model was tested on with ANN and decision tree. Overall accuracy value of ANN is 96% where the accuracy value of Decision tree is 91.5%. Hence the results obtained from ANN can be considered more efficient. Novelty: The number of models developed for crop recommendation is limited and the proposed model serves as the promising aspect in the planning of crops.

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“…In such cases Continuous Bag of Words and Skip‐gram models are applied. However, vectors obtained through such models do not consider words arrangements, context of the words and other words syntaxes such as POS tags 16 . For effective feature extraction and representation for sentence level sentiment classification, it is crucial to include other linguistic aspects such as POS tags, word semantic orientations and arrangement.…”

Section: Introductionmentioning

confidence: 99%

Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis

Mutinda

Mwangi

Okeyo

2021

Engineering Reports

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Sentiment analysis of social media textual posts can provide information and knowledge that is applicable in social settings, business intelligence, evaluation of citizens' opinions in governance, and in mood triggered devices in the Internet of Things. Feature extraction and selection is a key determinant of accuracy and computational cost of machine learning models for such analysis. Most feature extraction and selection techniques utilize bag of words, N‐grams, and frequency‐based algorithms especially Term Frequency‐Inverse Document Frequency. However, these approaches do not consider relationships between words, they ignore words' characteristics and they suffer high feature dimensionality. In this paper we propose and evaluate a feature extraction and selection approach that utilizes a fixed hybrid N‐gram window for feature extraction and minimum redundancy maximum relevance feature selection algorithm for sentence level sentiment analysis. The approach improves the existing features extraction techniques, specifically the N‐gram by generating a hybrid vector from words, Part of Speech (POS) tags, and word semantic orientation. The vector is extracted by using a static trigram window identified by a lexicon where a sentiment word appears in a sentence. A blend of the words, POS tags, and the sentiment orientations of the static trigram are used to build the feature vector. The optimal features from the vector are then selected using minimum redundancy maximum relevance (MRMR) algorithm. Experiments were carried out using the public Yelp dataset to compare the performance of the proposed model and existing feature extraction models (BOW, normal N‐grams and lexicon‐based bag of words semantic orientations). Using supervised machine learning classifiers the experimental results showed that the proposed model had the highest F‐measure (88.64%) compared to the highest (83.55%) from baseline approaches. Wilcoxon test carried out ascertained that the proposed approach performed significantly better than the baseline approaches. Comparative performance analysis with other datasets further affirmed that the proposed approach is generalizable.

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Multi-Label Classification of Microblogging Texts Using Convolution Neural Network

Cited by 44 publications

References 16 publications

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback

Artificial Neural Networks Based Integrated Crop Recommendation System Using Soil and Climatic Parameters

Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis

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