Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis

Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander; Hussain, Amir

doi:10.1007/s13278-021-00840-1

Cited by 17 publications

(5 citation statements)

References 45 publications

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“…Naive Bayes, a family of probabilistic classifiers based on Bayes' theorem, assumes independence between features for simplicity [53]. Class probability is calculated by considering the probability of each feature occurring within that class and dividing it by the probability of the feature itself; see Equation (12). These classifiers are scalable, requiring parameters proportional to the dataset's number of variables, making them suitable for large datasets.…”

Section: Naive Bayes (Nb)mentioning

confidence: 99%

“…Automatic text annotations can detect hate speech by applying machine learning methods with a semi-supervised learning approach [4,5]. Hate speech data are annotated using two categories (hate and not hate) [6][7][8][9][10], and using sentiment analysis methods, in which data are labeled using two or three categories, namely (positive and negative) [11,12], or (positive, negative, and neutral) [13][14][15][16]. We develop automatic annotations by utilizing a dataset with minimal labeled training data and incorporate self-learning for labels.…”

Section: Introductionmentioning

confidence: 99%

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Automated Text Annotation Using a Semi-Supervised Approach with Meta Vectorizer and Machine Learning Algorithms for Hate Speech Detection

Saifullah,

Dreżewski,

Dwiyanto

et al. 2024

Applied Sciences

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Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has various drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness. In addition, annotators may annotate ambiguous data. Therefore, we have developed the concept of automated annotation to get the best annotations using several machine-learning approaches. The proposed approach is based on an ensemble algorithm of meta-learners and meta-vectorizer techniques. The approach employs a semi-supervised learning technique for automated annotation to detect hate speech. This involves leveraging various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB), in conjunction with Word2Vec and TF-IDF text extraction methods. The annotation process is performed using 13,169 Indonesian YouTube comments data. The proposed model used a Stemming approach using data from Sastrawi and new data of 2245 words. Semi-supervised learning uses 5%, 10%, and 20% of labeled data compared to performing labeling based on 80% of the datasets. In semi-supervised learning, the model learns from the labeled data, which provides explicit information, and the unlabeled data, which offers implicit insights. This hybrid approach enables the model to generalize and make informed predictions even when limited labeled data is available (based on self-learning). Ultimately, this enhances its ability to handle real-world scenarios with scarce annotated information. In addition, the proposed method uses a variety of thresholds for matching words labeled with hate speech ranging from 0.6, 0.7, 0.8, to 0.9. The experiments indicated that the DT-TF-IDF model has the best accuracy value of 97.1% with a scenario of 5%:80%:0.9. However, several other methods have accuracy above 90%, such as SVM (TF-IDF and Word2Vec) and KNN (Word2Vec), based on both text extraction methods in several test scenarios.

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Section: Naive Bayes (Nb)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Text Annotation Using a Semi-Supervised Approach with Meta Vectorizer and Machine Learning Algorithms for Hate Speech Detection

Saifullah,

Dreżewski,

Dwiyanto

et al. 2024

Applied Sciences

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“…In addition to idiom detection, the classification method has also been applied to the comprehension of idioms, encompassing a variety of subjects. One of them is the classification of different sentiments conveyed through idiomatic expressions (Dashtipour et al, 2022). Jhamtani et al (2021) investigated whether dialogue models are able to handle figurative language usage and concluded that they do not perform well in this area.…”

Section: Idioms-related Classification Tasksmentioning

confidence: 99%

Generating Continuations in Multilingual Idiomatic Contexts

Pokharel,

Agrawal

2023

Proceedings of the 3rd Workshop on Multi-Lingual Representation Learning (MRL)

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The ability to process idiomatic or literal multiword expressions is a crucial aspect of understanding and generating any language. The task of generating contextually relevant continuations for narratives containing idiomatic (or literal) expressions can allow us to test the ability of generative language models (LMs) in understanding nuanced language containing noncompositional figurative text. We conduct a series of experiments using datasets in two distinct languages (English and Portuguese) under three different training settings (zero-shot, fewshot, and fine-tuned). Our results suggest that the models are only slightly better at generating continuations for literal contexts than idiomatic contexts, with exceedingly small margins. Furthermore, the models studied in this work perform equally well across both languages, indicating the robustness of generative models in performing this task.

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Section: Introductionmentioning

confidence: 99%

Proceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL)

2023

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Multimodal large models have been recognizedfor their advantages in various performance and downstream tasks. The development of these models is crucial towards achieving general artificial intelligence in the future. In this paper, we propose a novel universal language representation learning method called UniB-riVL, which is based on Bridging-Vision-and-Language (BriVL). Universal BriVL embeds audio, image, and text into a shared space, enabling the realization of various multimodal applications. Our approach addresses major challenges in robust language (both text and audio) representation learning and effectively captures the correlation between audio and image. Additionally, we demonstrate the qualitative evaluation of the generated images from UniBriVL, which serves to highlight the potential of our approach in creating images from audio. Overall, our experimental results demonstrate the efficacy of UniBriVL in downstream tasks and its ability to choose appropriate images from audio. The proposed approach has the potential for various applications such as speech recognition, music signal processing, and captioning systems.

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Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis

Cited by 17 publications

References 45 publications

Automated Text Annotation Using a Semi-Supervised Approach with Meta Vectorizer and Machine Learning Algorithms for Hate Speech Detection

Automated Text Annotation Using a Semi-Supervised Approach with Meta Vectorizer and Machine Learning Algorithms for Hate Speech Detection

Generating Continuations in Multilingual Idiomatic Contexts

Proceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL)

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