Design and Implementation of Grievance Filing Application using Automatic Text Classification

This research aims to design a Grievance Filing System built using automatic text classification without any manual interruption. Various methodologies are followed to achieve it and are implemented. Performance of the different algorithms is discussed. People are less aware about the lengthy methods for lodging complaints. We propose a simplified process of enrolling grievances to ministries. The system accepts grievances in recorded voice form. The system is designed for Marathi language. Input in the form of speech will ease people’s comfort for lodging grievances. We present a model where voice is first preprocessed, followed by text classification using deep learning approaches such as CNN and LSTM, the grievances will be sent to respective ministry. This system can be used by government ministries to get grievances from common people through a simplified process. User will be notified on the progress of their lodged complaint and on its successful resolution by respective ministry.

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Word embedding and text classification based on deep learning methods

MATEC Web of Conferences ◽

10.1051/matecconf/202133606022 ◽

2021 ◽

Vol 336 ◽

pp. 06022

Author(s):

Saihan Li ◽

Bing Gong

Keyword(s):

Deep Learning ◽

Text Classification ◽

Word Embedding ◽

Learning Technology ◽

Automatic Text Classification ◽

Data Volume ◽

Text Information ◽

Industry Area ◽

Automatic Text ◽

Embedding Methods

Traditional manual text classification method has been unable to cope with the current huge amount of data volume. The improvement of deep learning technology also accelerates the technology of text classification. Based on this background, we presented different word embedding methods such as word2vec, doc2vec, tfidf and embedding layer. After word embedding, we demonstrated 8 deep learning models to classify the news text automatically and compare the accuracy of all the models, the model ‘2 layer GRU model with pretrained word2vec embeddings’ model got the highest accuracy. Automatic text classification can help people summary the text accurately and quickly from the mass of text information. No matter in the academic or in the industry area, it is a topic worth discussing.

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Toward a multi-sensor neural net approach to automatic text classification

Advanced IT Tools ◽

10.1007/978-0-387-34979-4_41 ◽

1996 ◽

pp. 367-373

Author(s):

Venu Dasigi ◽

Reinhold C. Mann

Keyword(s):

Text Classification ◽

Neural Net ◽

Automatic Text Classification ◽

Automatic Text

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Toward a multi-sensor-based approach to automatic text classification

10.2172/130610 ◽

1995 ◽

Author(s):

V.R. Dasigi ◽

R.C. Mann

Keyword(s):

Text Classification ◽

Automatic Text Classification ◽

Automatic Text

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The VINITI RAS Automatic Text Classification System for Processing the Flow of Scientific Publications

Automatic Documentation and Mathematical Linguistics ◽

10.3103/s0005105520030048 ◽

2020 ◽

Vol 54 (3) ◽

pp. 113-123

Author(s):

V. S. Egorov ◽

E. S. Kozlova ◽

K. E. Lomotin ◽

O. V. Fedorets ◽

A. V. Filimonov ◽

...

Keyword(s):

Text Classification ◽

Classification System ◽

Scientific Publications ◽

Automatic Text Classification ◽

Viniti Ras ◽

Automatic Text

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Automated News Classification using N-gram Model and Key Features of Nepali Language

SCITECH Nepal ◽

10.3126/scitech.v13i1.23504 ◽

2018 ◽

Vol 13 (1) ◽

pp. 64-69

Author(s):

Dinesh Dangol ◽

Rupesh Dahi Shrestha ◽

Arun Timalsina

Keyword(s):

Text Classification ◽

English Language ◽

Promising Result ◽

Text Processing ◽

Automatic Text Classification ◽

Key Features ◽

Research Experiment ◽

N Gram ◽

Automated Text Processing ◽

Automatic Text

With an increasing trend of publishing news online on website, automatic text processing becomes more and more important. Automatic text classification has been a focus of many researchers in different languages for decades. There is a huge amount of research repository on features of English language and their uses on automated text processing. This research implements Nepali language key features for automatic text classification of Nepali news. In particular, the study on impact of Nepali language based features, which are extremely different than English language is more challenging because of the higher level of complexity to be resolved. The research experiment using vector space model, n-gram model and key feature based processing specific to Nepali language shows promising result compared to bag-of-words model for the task of automated Nepali news classification.

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