Ranking of Odia Text Document Relevant to User Query Using Vector Space Model

Parida, Upasana; Nayak, Mamata; Nayak, Ajit Ku.

doi:10.1109/icaml48257.2019.00039

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…In this paper, research is conducted with the steps that can be seen in Figure 1. The first step taken is to enter keywords (can be in the form of words, phrases or sentences), then do the preprocessing to get a list of word tokens which then do the weight calculation tf-idf after it obtained a list of queries and documents weights used in the computation algorithm VSM [13][14][15][16] if the cosine value>0 then shown a list of relevant data otherwise found no data is displayed. This research uses a software development method called the prototype model [17][18][19].…”

Section: Methodsmentioning

confidence: 99%

The search for science and technology verses in Qur’an and hadith

et al. 2021

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Currently, the Vector Space Model algorithm has been widely implemented for the document search feature because of its reliability in retrieving information. One of them in the search for verses of the Qur'an based on the translation. However, if the phrase or word used is different (even though it has one meaning) with the word in the document in the database, the system will not display the verse. As we know that the Qur'an has a very deep meaning, so an interpretation of the verse is needed. Therefore, this research focuses on implementing the Vector Space Model (VSM) algorithm for searching verses and hadiths in science and technology by using the discussion parameters of these verses or hadiths. The test results obtained with 20 keyword samples using metric recall were 81% with an average time of 2.24 seconds.

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Section: Methodsmentioning

confidence: 99%

The search for science and technology verses in Qur’an and hadith

et al. 2021

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“…The Vector Space Model is a linear mathematical model that converts texts into numbers [31]. This process transforms unstructured data into the structured set using the weights of terms as a feature of the text.…”

Section: Vector Space Modelmentioning

confidence: 99%

Analysis of Consumer Sentiments towards Online Shopping Using Context-Free Grammar and Deep Learning

Chanakot,

Phoksawat

2024

RIA

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Consumer reviews represent customer sentiment. They provide valuable insights that businesses can use to improve product or service quality and meet customer needs. Therefore, this research presents an analysis of consumer sentiments towards online shopping using Context-Free Grammar and Deep Learning. It tested the effectiveness of classifying consumer sentiments from 3,600 product or service reviews. The dataset included three types of sentiment categories: Positive, Negative, and Neutral. Context-Free Grammar (CFG) was used to assign term functions as sentiment indicators, and Term Frequency-Inverse Document Frequency (TF-IDF) was used for feature selection. A threshold value was then assigned to each term that represented the sentiment categories. The dataset was divided into 15-fold cross-validation to test the effectiveness of the model before Deep Learning algorithm was used to classify the sentiments. Deep Learning algorithms have the capability to learn complex relationships between terms, allowing for precise sentiment classification. The evaluation showed that using CFG and TF-IDF for term weighting improved the selection of keyword features, leading to significantly more precise sentiment classification. The average Precision, Recall, and F-measure were higher than exclusively using TF-IDF. Moreover, the determination of an appropriate threshold value reduced data complexity without affecting the accuracy of sentiment classification.

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“…Because the short text is generally less than 100 words, the short length makes the short text contain fewer features, so the converted data have the characteristics of high dimension and sparseness. In traditional short text classification, the one-hot method [2] or constructing a text vector space model [3] is often used to represent text. These methods are simple and effective but also easily cause dimensional disasters [4].…”

Section: Introductionmentioning

confidence: 99%

Short Text Classification Based on Explicit and Implicit Multiscale Weighted Semantic Information

Gong,

Zhang,

Guo

et al. 2023

Symmetry

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Considering the poor effect of short text classification due to insufficient semantic information mining in the current short text matching methods, a new short text classification method is proposed based on explicit and implicit multiscale weighting semantic information interaction. First, the explicit and implicit representations of short text are obtained by a word vector model (word2vec), convolutional neural networks (CNNs), and long short-term memory (LSTM). Then, a multiscale convolutional neural network obtains the explicit and implicit multiscale weighting semantics information of short text. Finally, the multiscale weighting semantics is fused for more accurate short text classification. The experimental results show that this method is superior to the existing classical short text classification algorithms and two advanced short text classification models on the five short text classification datasets of MR, Subj, TREC, SST1 and SST2 with accuracies of 85.7%, 96.9%, 98.1%, 53.4% and 91.8%, respectively.

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Ranking of Odia Text Document Relevant to User Query Using Vector Space Model

Cited by 4 publications

References 9 publications

The search for science and technology verses in Qur’an and hadith

The search for science and technology verses in Qur’an and hadith

Analysis of Consumer Sentiments towards Online Shopping Using Context-Free Grammar and Deep Learning

Short Text Classification Based on Explicit and Implicit Multiscale Weighted Semantic Information

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