A multilingual fuzzy approach for classifying Twitter data using fuzzy logic and semantic similarity

Madani, Youness; Erritali, Mohammed; Bengourram, Jamaâ; Sailhan, Françoise

doi:10.1007/s00521-019-04357-9

Cited by 20 publications

(16 citation statements)

References 22 publications

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“…However, [17] utilized paraphrase identification and topic modelling for semantic analysis to measure the similarity between Arabic news tweets, and TREASURE [16] considered semantic features (using a Word Embedding Model) and syntactic features (counting Parts of Speech tags and Twitter-specific features) to measure the similarity of political tweets. [25] proposed classifying tweets based on a hybrid approach using sentiment analysis, fuzzy logic and semantic similarity using Wordnet.…”

Section: Related Workmentioning

confidence: 99%

A Semantic and Syntactic Similarity Measure for Political Tweets

et al. 2020

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Measurement of the semantic and syntactic similarity of human utterances is essential in allowing machines to understand dialogue with users. However, human language is complex, and the semantic meaning of an utterance is usually dependent upon the context at a given time and learnt experience of the meaning of the words that are used. This is particularly challenging when automatically understanding the meaning of social media, such as tweets, which can contain non-standard language. Short Text Semantic Similarity measures can be adapted to measure the degree of similarity of a pair of tweets. This work presents a new Semantic and Syntactic Similarity Measure (TSSSM) for political tweets. The approach uses word embeddings to determine semantic similarity and extracts syntactic features to overcome the limitations of current measures which may miss identical sequences of words. A large dataset of tweets focusing on the political domain were collected, pre-processed and used to train the word embedding model, with various experiments performed to determine the optimal model and parameters. A selection of tweet pairs were evaluated by humans for semantic equivalence and correlated against the measure. The new measure can be used in a variety of applications, including for identifying and analyzing political narratives. Experiments on three diverse human-labelled test datasets demonstrate that the measure outperforms an existing measure, performs well on tweets from the political domain and may also generalize outside the political domain.

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Section: Related Workmentioning

confidence: 99%

A Semantic and Syntactic Similarity Measure for Political Tweets

et al. 2020

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“…MapReduce. In Hadoop system, the operation can be divided into two stages, Map and Reduce respectively [14]. During the Map stage, a set of intermediate key-value pairs based on the input key-value is generated.…”

Section: Plos Onementioning

confidence: 99%

Fault diagnosis method of submersible screw pump based on random forest

et al. 2020

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The difficulty in directly determining the failure mode of the submersible screw pump will shorten the life of the system and the normal production of the oil well. This thesis aims to identify the fault forms of submersible screw pump accurately and efficiently, and proposes a fault diagnosis method of the submersible screw pump based on random forest. HDFS storage system and MapReduce processing system are established based on Hadoop big data processing platform; Furthermore, the Bagging algorithm is used to collect the training set data. Also, this thesis adopts the CART method to establish the sample library and the decision trees for a random forest model. Six continuous variables, four categorical variables and fault categories of submersible screw pump oil production system are used for training the decision trees. As several decision trees constitute a random forest model, the parameters to be tested are input into the random forest models, and various types of decision trees are used to determine the failure category in the submersible screw pump. It has been verified that the accuracy rate of fault diagnosis is 92.86%. This thesis can provide some meaningful guidance for timely detection of the causes of downhole unit failures, reducing oil well production losses, and accelerating the promotion and application of submersible screw pumps in oil fields.

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“…At present, the sentiment analysis uses the single language particularly, English, but due to a rapid increase in the usage of Internet, a massive growth is reported in the social media applications, such as Facebook, Twitter, Instagram and so forth the people adapt themselves to their mother tongue for posting and messaging, which insists a necessity for handling the multiple language-based reviews [8]. For example: In AirBnB, Amazon and TripAdvisor, there are around 400 billion of people, using these networks in a month.…”

Section: Introductionmentioning

confidence: 99%

“…Accounting this scenario, the distribution is unbalanced for different languages, with fewer reviews for some languages, resulting in the shortfall of the data in such a way that the existing sentiment analysis algorithms struggle to yield good results. Due to this inefficiency, the sentiment analysis algorithms depend on the languages with greater density of reviews, which leads to an increased risk of missing essential information in texts written in other languages [8]. Thus, the innovation of multilingual sentiment analysis techniques using multiple languages for the sentimental analysis by analyzing the data in different languages is more efficient when compared with the circumstances of using a single language [3,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Due to this inefficiency, the sentiment analysis algorithms depend on the languages with greater density of reviews, which leads to an increased risk of missing essential information in texts written in other languages [8]. Thus, the innovation of multilingual sentiment analysis techniques using multiple languages for the sentimental analysis by analyzing the data in different languages is more efficient when compared with the circumstances of using a single language [3,8,9].…”

Section: Introductionmentioning

confidence: 99%

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Multilingual Sentiment Analysis Using the Social Eagle-Based Bidirectional Long Short-Term Memory

2022

IJIES

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Rapid evolvement in the field of social media constitutes an increasing demand in sentiment prediction for effective communication. Sentimental analyses are employed to extract opinions from individuals in order to invigorate the quality of material or a product for the effective growth of an organization. Researchers are aspired to predict the sentiment through comments of different languages, but there is a lack in the dictionaries accessible for different languages. In this research, hybridized Social Eagle Algorithm-based deep bidirectional long short-term memory (SoEo Algorithm-based deep BiLSTM) is proposed to predict the sentiment effectively. The languages are identified and converted into a standard format by the process of Transliteration and the features are extracted from these standardized data. The hunting strategy of the bald eagle and the adaptation behavior of coyotes are hybridized and executed in both forward and backward directions utilizing the BiLSTM classifier. The simulation outcome shows that the proposed model obtained an accuracy of 91.572%, precision of 89.196 %, recall of 91.551 % and F1 measure of 89.019%, which will be more efficient compared to the state-of-art methods.

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A multilingual fuzzy approach for classifying Twitter data using fuzzy logic and semantic similarity

Cited by 20 publications

References 22 publications

A Semantic and Syntactic Similarity Measure for Political Tweets

A Semantic and Syntactic Similarity Measure for Political Tweets

Fault diagnosis method of submersible screw pump based on random forest

Multilingual Sentiment Analysis Using the Social Eagle-Based Bidirectional Long Short-Term Memory

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