126,536 research outputs found
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
Fine-grained Affective Processing Capabilities Emerging from Large Language Models
Large language models, in particular generative pre-trained transformers
(GPTs), show impressive results on a wide variety of language-related tasks. In
this paper, we explore ChatGPT's zero-shot ability to perform affective
computing tasks using prompting alone. We show that ChatGPT a) performs
meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions,
b) has meaningful emotion representations in terms of emotion categories and
these affective dimensions, and c) can perform basic appraisal-based emotion
elicitation of situations based on a prompt-based computational implementation
of the OCC appraisal model. These findings are highly relevant: First, they
show that the ability to solve complex affect processing tasks emerges from
language-based token prediction trained on extensive data sets. Second, they
show the potential of large language models for simulating, processing and
analyzing human emotions, which has important implications for various
applications such as sentiment analysis, socially interactive agents, and
social robotics
Contextual information usage for the enhancement of basic emotion classification in a weakly labelled social network dataset in Spanish
Basic emotion classification is one of the main tasks of Sentiment Analysis usually performed by using several machine learning techniques. One of the main issues in Sentiment Analysis is the availability of tagged resources to properly train supervised classification algorithms. This is of particular concern in languages other than English, such as Spanish, where scarcity of these resources is the norm. In addition, most basic emotion datasets available in Spanish are rather small, containing a few hundred (or thousand) samples. Usually, the samples only contain a short text (frequently a comment) and a tag (the basic emotion), omitting crucial contextual information that may help to improve the classification task results. In this paper, the impact of using contextual information is measured on a recently published Spanish basic emotion dataset and the baseline architecture proposed in the Semantic Evaluation 2019 competition. This particular dataset has two main advantages for this paper. First, it was compiled using Distant Supervision and as a result it contains several hundred thousand samples. Secondly, the authors included valuable contextual information for each comment. The results show that contextual information, such as news headlines or summaries, helps improve the classification accuracy over a dataset of distantly supervised basic emotion labelled comments
The logic and linguistic model for automatic extraction of collocation similarity
The article discusses the process of automatic identification of collocation similarity. The semantic analysis is one of the most advanced as well as the most difficult NLP task. The main problem of semantic processing is the determination of polysemy and synonymy of linguistic units. In addition, the task becomes complicated in case of word collocations. The paper suggests a logical and linguistic model for automatic determining semantic similarity between colocations in Ukraine and English languages. The proposed model formalizes semantic equivalence of collocations by means of semantic and grammatical characteristics of collocates. The basic idea of this approach is that morphological, syntactic and semantic characteristics of lexical units are to be taken into account for the identification of collocation similarity. Basic mathematical means of our model are logical-algebraic equations of the finite predicates algebra. Verb-noun and noun-adjective collocations in Ukrainian and English languages consist of words belonged to main parts of speech. These collocations are examined in the model. The model allows extracting semantically equivalent collocations from semi-structured and non-structured texts. Implementations of the model will allow to automatically recognize semantically equivalent collocations. Usage of the model allows increasing the effectiveness of natural language processing tasks such as information extraction, ontology generation, sentiment analysis and some others
Undivided Attention: Are Intermediate Layers Necessary for BERT?
In recent times, BERT-based models have been extremely successful in solving
a variety of natural language processing (NLP) tasks such as reading
comprehension, natural language inference, sentiment analysis, etc. All
BERT-based architectures have a self-attention block followed by a block of
intermediate layers as the basic building component. However, a strong
justification for the inclusion of these intermediate layers remains missing in
the literature. In this work we investigate the importance of intermediate
layers on the overall network performance of downstream tasks. We show that
reducing the number of intermediate layers and modifying the architecture for
BERT-Base results in minimal loss in fine-tuning accuracy for downstream tasks
while decreasing the number of parameters and training time of the model.
Additionally, we use the central kernel alignment (CKA) similarity metric and
probing classifiers to demonstrate that removing intermediate layers has little
impact on the learned self-attention representations
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