Sentiment analysis of Romanized Sindhi text

Sodhar, Irum Naz; Jalbani, Akhtar Hussain; Buller, Abdul Hafeez; Channa, Muhammad Ibrahim; Hakro, Dil Nawaz

doi:10.3233/jifs-179675

Cited by 8 publications

(13 citation statements)

References 8 publications

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“…Structure of sentiment analysis of RST is same as English language [10]. The structure of Romanized Sindhi Text depend upon the three main attribute of grammar as like: subject à Verb à Object, same as like an English sentence [11].…”

Section: Structure Of Romanized Sindhi Textmentioning

confidence: 99%

Hybrid Approach Used to Analyze the Sentiments of Romanized Text (Sindhi)

Sodhar¹,

Sulaiman²,

Buller³

et al. 2023

IJACSA

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Sentiment analysis is an important part of natural language processing (NLP). This study evaluated the sentiment of Romanized Sindhi Text (RST) using a hybrid approach and ground truth values. The methodology of sentiment analysis involves three major steps: input data, process on tool, analysis of data and evaluation of results. One hundred RST sentences were used in this study's sentiment analysis, which can be positive, neutral, or negative. The statements in the corpus of this study are simple to understand and are used in everyday life. This research used an online Python tool to process a text and get results in the form of outcomes. The results showed that 86% of the sentences have neutral sentiments, 9% of the total results of sentiment analysis have negative sentiments, and only 5% of sentences of Romanized Sindhi Text have positive sentiments. The accuracy of the RST was measured on an online calculator and the value was 87.02% on the basis of ground truth values. An error ratio of 12.98% was calculated on the basis accuracy found on the online calculator of confusion matrix.

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Section: Structure Of Romanized Sindhi Textmentioning

confidence: 99%

Hybrid Approach Used to Analyze the Sentiments of Romanized Text (Sindhi)

Sodhar¹,

Sulaiman²,

Buller³

et al. 2023

IJACSA

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“…The translators are digital in AI that not only used for translation but different meaning of word and also help for analysis of sentiments from word. Sentiments analysis is the task of Artificial intelligence [1] [2]. Now a day's Artificial intelligence mostly used in portable devices.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…Simply translation is the way of to connect two persons/peoples by using through translator, when both users used different languages. Translation is very helpful for almost every person who uses social media applications [15]. In social media application people used different language everyone cannot understand that's why immediately need of translator, that time very helpful and provide way of communication with almost every person.…”

Section: Ai In Translation Of Textmentioning

confidence: 99%

Identification of Online Statistical Translation and Text Issues in Communication Technologies

2021

IJATCSE

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Now-a-days Artificial Intelligence growing fast towards new communication technologies and it provide easiness to the users as per there demand. Mobile/cell phone is one of the most reliable examples of latest technologies. It is easy to communicate with peoples of different countries and also in different languages. For the communications different technologies were used to solve this major issue around the globe. Translation is one of them to translate the one language to another language. The research methodology is based on the five phases. In this research study, analysis of the text by process on online translator, bilingual corpus (English and Sindhi) were used. After the translation from English to Sindh and Translation from Sindhi to English many issues were faced at the various communication technologies. When input data of large sentence to the translator it takes more time for the translation, but mostly this issue came in translation of text from English to Sindhi. Moreover, the results are based on translated text of English Languages and total three hundred fifty two words (352) were used.

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“…Entrepreneurship process theory is a very important theory in the field of entrepreneurship. It makes entrepreneurial activity no longer a “black box.” People can analyze the different stages of the entrepreneurial process and explore the impact of different stages on entrepreneurial performance, making the part and the whole static Take into account the dynamics (Lian et al, 2020 ; Sodhar et al, 2020 ).…”

Section: Dynamic Evolution Mechanism Of Digital Entrepreneurship Ecosystem Based On Text Sentiment Calculation Analysismentioning

confidence: 99%

Dynamic Evolution Mechanism of Digital Entrepreneurship Ecosystem Based on Text Sentiment Computing Analysis

Yao

2021

Front. Psychol.

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To solve the limitations of the current entrepreneurial ecosystem, the research on the digital entrepreneurial ecosystem is more meaningful. This article aims to study the dynamic evolution mechanism of the digital entrepreneurship ecosystem based on text sentiment computing analysis. It proposes an improved Bi-directional long short-term memory (Bi-LSTM) model, which uses a multilayer neural network to deal with classification problems. It has a higher accuracy rate, recall rate, and F1 value than the traditional LSTM model and can better perform sentiment analysis on text. The algorithm uses the optimized Naive Bayes algorithm, which is based on Euclidean distance weighting and can assign different weights to the final classification results according to different attributes. Compared with the general Bayes algorithm, it improves the calculation efficiency and can better match the digital entrepreneurial ecosystem, which is evolving dynamically, predicting and analyzing its future development. The experimental results in this article show that the improved Bi-LSTM is better than the traditional Bi-LSTM model in terms of accuracy and F1 value. The accuracy rate is increased by 1.1%, the F1 value is increased by 0.6%, and the recall rate is only <0.2%. Running on the Spark platform, although 3% accuracy is sacrificed, the running time is increased by 320%. Compared with the traditional cellular neural network (CNN) algorithm, the accuracy rate is increased by 4%, the recall rate is increased by 14%, and the F1 value is increased by 9%, which proves that it has a strong non-linear fitting ability. The performance improvement brought by the huge data set is very huge, which fully proves the feasibility of the digital entrepreneurship ecosystem.

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Sentiment analysis of Romanized Sindhi text

Cited by 8 publications

References 8 publications

Hybrid Approach Used to Analyze the Sentiments of Romanized Text (Sindhi)

Hybrid Approach Used to Analyze the Sentiments of Romanized Text (Sindhi)

Identification of Online Statistical Translation and Text Issues in Communication Technologies

Dynamic Evolution Mechanism of Digital Entrepreneurship Ecosystem Based on Text Sentiment Computing Analysis

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