Affective — Hierarchical classification of text — An approach using NLP toolkit

SeshathriAathithyan, S.; Sriram, M.; Prasanna, S.; Venkatesan, R.

doi:10.1109/iccpct.2016.7530228

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…The tweets were further processed using NLP tools and the feature engineering process as discussed in sections 3.2 and 3.3. NLTK 3.1 [33] was used for building python programs as the work involved human generated textual dataset. After pre-processing the dataset, the input was fed to enhanced K-means clustering algorithm as discussed in section 3.4.…”

Section: And Discussionmentioning

confidence: 99%

Root cause analysis of COVID-19 cases by enhanced text mining process

Kokatnoor

Balachandran

2022

IJECE

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<p>The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the public’s perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter®. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease.</p>

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Section: And Discussionmentioning

confidence: 99%

Root cause analysis of COVID-19 cases by enhanced text mining process

Kokatnoor

Balachandran

2022

IJECE

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“…Moreover, surveys should be conducted which include budget constraints and its impact should be highlighted. Seshathri Aathithyan et al [6] presented a "Hierarchical Classification of Text -An Approach Using NLP Toolkit" in 2016. Text acquisition was done using NLP toolkit in python.…”

Section: Literature Surveymentioning

confidence: 99%

Food Recommendation using Classifier and Modified Apriori Algorithm

Dasgupta*¹,

Karmakar²

2019

IJITEE

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In today’s world, computer technologies have advanced a lot. One of its greatest gifts to the world is Artificial Intelligence. Natural Language Processing (NLP) and Machine Learning (ML) are two of its subdomains. In this paper, modified versions of two common NLP and ML algorithms have been used to classify food reviews and provide suitable recommendations from them. Currently, reviews can be classified into positive and negative reviews, but it becomes difficult when one review says positive about item A and negative about item B. Moreover, the current Apriori algorithm doesn’t consider the feedbacks from customers (reviews). Modified classifier algorithm and consequently, modified Apriori algorithm has been used to classify each statement part by part and provide recommendations, not just on previous purchases but also using the reviews about above-mentioned purchases. The algorithms can be used for purposes other than food analysis also – wherever purchases and reviews are involved. For e.g., e-commerce companies can use the algorithms to predict and recommend suitable items a user may be interested in.

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