SMS sentiment classification using an evolutionary optimization based fuzzy recurrent neural network

Sentiment analysis using the inbox message polarity is a challenging task in text mining, this analysis is used to differentiate spam and ham messages in mail. Polarity estimation is mandatory for spam and ham identification, whereas developing a perfect architecture for such classification is the hot demanding topic. To fulfill that, fuzzy based Recurrent Neural network-based Harris Hawk optimization (FRNN-HHO) is introduced, which performs post-classification over the classified messages (spam and ham). Previously the authors tried to classify the spam and ham messages from the collection of SMSs. But sometimes, the spam messages may incorrectly be classified within the ham classes. This misclassification may reduce the accuracy. The sentiment analysis process is performed over the classified messages to improve such classification accuracy. The spam and ham messages from the available data are classified using a Kernel Extreme Learning Machine (KELM) classifier. The sentiment analysis and classification based experimental evaluation is carried out using accuracy, recall, f-measure, precision, RMSE, and MAE. The performance of the proposed architecture is evaluated using threedifferent datasets: SMS, Email, and spam-assassin. The Area under the curve (AUC) of the proposed approach is found to be 0.9699 (SMS dataset), 0.958 (Email dataset), and 0.95 (spam assassin).

AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis

Sentiment analysis involves extricating and interpreting people's views, feelings, beliefs, etc., about diverse actualities such as services, goods, and topics. People intend to investigate the users' opinions on the online platform to achieve better performance. Regardless, the high-dimensional feature set in an online review study affects the interpretation of classification. Several studies have implemented different feature selection techniques; however, getting a high accuracy with a very minimal number of features is yet to be accomplished. This paper develops an effective hybrid approach based on an enhanced genetic algorithm (GA) and analysis of variance (ANOVA) to achieve this purpose. To beat the local minima convergence problem, this paper uses a unique two-phase crossover and impressive selection approach, gaining high exploration and fast convergence of the model. The use of ANOVA drastically reduces the feature size to minimize the computational burden of the model. Experiments are performed to estimate the algorithm performance using different conventional classifiers and algorithms like GA, Particle Swarm Optimization (PSO), Recursive Feature Elimination (RFE), Random Forest, ExtraTree, AdaBoost, GradientBoost, and XGBoost. The proposed novel approach gives impressive results using the Amazon Review dataset with an accuracy of 78.60 %, F1 score of 79.38 %, and an average precision of 0.87, and the Restaurant Customer Review dataset with an accuracy of 77.70 %, F1 score of 78.24 %, and average precision of 0.89 as compared to other existing algorithms. The result shows that the proposed model outperforms other algorithms with nearly 45 and 42% fewer features for the Amazon Review and Restaurant Customer Review datasets.

PubExN: An Automated PubMed Bulk Article Extractor with Affiliation Normalization Package

Biomedical article extraction is the preliminary step for every biomedical application. These applications are helpful in finding the gene, disease, chemical, drugs, protein entities. Finding entities relation such as gene-gene entities, drug-disease interaction, and chemical protein relation the PubExN can be helpful for these types of biomedical applications. In most cases, domain experts do this extraction process on their own. Human interference makes this process time-consuming and there is a high probability, that documents can be missed during the extraction process. To get rid of these complicated processes a python package is introduced to automate the process of bulk extraction from the PubMed database. The extraction process covers all the citation information with the associated abstract. The batch approach is used to extract the bulk extraction. The motivation for the development of PubExN was to provide flexibility for the extraction process of biomedical article's text data from NCBI's PubMed database. Basically, NCBI's PubMed database article contains the article id or can say PubMed-id (PMID), the title of the article, abstract, authors information, etc. This package will benefit many biomedical texts mining research including biomedical named entity recognition, biomedical relation extraction, literature discovery, knowledgebase creation, and various biomedical Natural Language Processing (NLP) tasks. In addition, it could be used in the author name disambiguation problems and new drug discoveries. This package will help save time and extra effort for the extraction and normalization process of PubMed articles.

ABEE: automated bio entity extraction from biomedical text documents

Purpose

The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.

Design/methodology/approach

In the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text.

Findings

The proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments.

Research limitations/implications

As such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups.

Practical implications

As far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery.

Social implications

During the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area.

Originality/value

In this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.

Statistical Growth Analysis of Rice Plants in Chhattisgarh Region Using Automated Pixel-Based Mapping Technique

The statistical growth analysis of field crop has become a great challenge in agriculture. Analyzing the growth of crop through automation provides extensive significance to the farmers for getting information about the problem arising in plants due to irregular growth monitoring. The idea behind this work is the importance of mapping with pixel-based clustering technique for growth analysis in terms of height calculation of rice crop (rice variety is MTU-1010). Height measurement plays a vital role in regular assessment for a healthy crop, and the approach proposed in this work achieves 97.58% accuracy of 14 sampled datasets taken from Indira Gandhi Agriculture University of Raipur, Chhattisgarh; a real-time dataset has been prepared. Proposed work is used for analyzing vertical as well as horizontal scaling technique. Vertical mapping provides the height of a single plant whereas horizontal mapping using k-means clustering provides an average height of the whole field. This work uses machine learning, and image processing techniques are used for this work.

Prospecting the Effect of Topic Modeling in Information Retrieval

Enormous records and data are gathered every day. Organization of this data is a challenging task. Topic modeling provides a way to categorize these documents, where high dimensionality of the corpus affects the result of topic model, making it important to apply feature selection or information retrieval process for dimensionality reduction. The requirement for efficient topic modeling includes the removal of unrelated words that might lead to specious coexistence of the unrelated words. This paper proposes an efficient framework for the generation of better topic coherence, where term frequency-inverse document frequency (TF-IDF) and parsimonious language model (PLM) are used for the information retrieval task. PLM extracts the important information and expels the general words from the corpus, whereas TF-IDF re-estimates the weightage of each word in the corpus. The work carried out in this paper improved the topic coherence measure to provide a better correlation among the actual topic and the topics generated from PLM.

SMS spam filtering and thread identification using bi-level text classification and clustering techniques

SMS spam detection is an important task where spam SMS messages are identified and filtered. As greater numbers of SMS messages are communicated every day, it is very difficult for a user to remember and correlate the newer SMS messages received in context to previously received SMS. SMS threads provide a solution to this problem. In this work the problem of SMS spam detection and thread identification is discussed and a state of the art clustering-based algorithm is presented. The work is planned in two stages. In the first stage the binary classification technique is applied to categorize SMS messages into two categories namely, spam and non-spam SMS; then, in the second stage, SMS clusters are created for non-spam SMS messages using non-negative matrix factorization and K-means clustering techniques. A threading-based similarity feature, that is, time between consecutive communications, is described for the identification of SMS threads, and the impact of the time threshold in thread identification is also analysed experimentally. Performance parameters like accuracy, precision, recall and F-measure are also evaluated. The SMS threads identified in this proposed work can be used in applications like SMS thread summarization, SMS folder classification and other SMS management-related tasks.

Email thread identification using latent Dirichlet allocation and non-negative matrix factorization based clustering techniques

Emails are the most popular and effective way of communicating over the internet. A number of applications are available today for computers and mobile devices for email messaging. Email messaging is constantly getting more popular and, as a result, numbers of sent and received emails are also increasing. It is very difficult for a user to remember emails and relate newer incoming emails to previous communications made on similar topics. Email threads provide a mechanism using which a user can obtain sequences of emails for a particular set of communication in a time frame and provides a number of benefits to users. In this work two email thread identification algorithms based on a nested textual clustering approach are presented. The work is planned in two stages; in the first stage two popular text clustering approaches, latent Dirichlet allocation and non-negative matrix factorization, are applied over the email messages to form the email clusters. Then in the second stage, clustering is again performed over the created email clusters to identify the email threads using threading features. Performance parameters like accuracy, precision, recall and F-measure are evaluated for the presented thread identification algorithms.