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Short texts refer to textual data that consists of a small number of words or characters. Unlike longer texts, which can span multiple paragraphs or pages, short texts are typically concise and contain limited information.
Short texts can take various forms, including social media posts, tweets, chat messages, product reviews, headlines, and search queries. These texts are often characterized by their brevity, which presents unique challenges for natural language processing (NLP) tasks and analysis.
Key characteristics of short texts:
1. Lack of context: Short texts often lack the surrounding context that longer texts provide. They may not contain explicit information about the topic, background, or context of the communication. This absence of context can make it more challenging to understand the intended meaning or perform accurate analysis.
2. Informal language: Short texts tend to be written in a more casual and informal style, particularly in social media or messaging platforms. This can include the use of abbreviations, acronyms, slang, emoticons, or unconventional grammar and spelling. Understanding and processing such informal language can be difficult for NLP models.
3. Noisy and incomplete information: Due to their brevity, short texts often lack comprehensive information. They may only provide a snippet of a larger conversation or express an idea in a condensed form. Additionally, short texts can contain noise, such as typographical errors, misspellings, or incomplete sentences, which can further complicate NLP tasks.
4. Domain-specific challenges: Short texts in specific domains, such as medical or legal texts, can present additional challenges. These domains often have specialized vocabulary, technical terms, or jargon that may require domain-specific knowledge for accurate understanding and analysis.
Handling short texts in NLP tasks requires specialized techniques and models that can effectively capture the limited context and extract meaningful information from the available text. Techniques such as word embeddings, recurrent neural networks (RNNs), or transformer-based models like BERT or GPT have been employed to address the challenges associated with short texts.
Short text analysis finds applications in various areas, including sentiment analysis, topic classification, spam detection, chatbot systems, social media monitoring, and customer feedback analysis, among others.
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A multi-label dataset is a type of dataset where each data instance can be associated with multiple labels or categories simultaneously. In contrast to a single-label dataset, where each instance is assigned to only one label, multi-label datasets allow for more complex and nuanced classification tasks.
In a multi-label dataset, each data instance is typically represented by a set of features or attributes, and the associated labels are represented as binary indicators or multi-hot vectors. Each label corresponds to a specific category or class, and the binary indicator indicates whether the instance belongs to that particular category or not. For example, in a hate speech detection task, a multi-label dataset may include instances labeled with categories such as hate speech, offensive language, and abusive content, where each instance can be associated with one or more of these labels.
The presence of multiple labels in a dataset introduces additional complexity in the classification task. It allows for scenarios where an instance can belong to multiple categories simultaneously, capturing the multi-faceted nature of real-world problems. Multi-label classification techniques and models are specifically designed to handle such datasets and make predictions for multiple labels.
When working with multi-label datasets, evaluation metrics differ from those used in single-label classification. Common evaluation measures for multi-label classification include precision, recall, F1-score, and metrics like Hamming loss or subset accuracy. These metrics assess the model's performance in predicting each label independently and capturing the overall label dependencies.
Multi-label datasets are commonly used in various applications, such as text categorization, image classification, video tagging, and recommendation systems, where instances can belong to multiple categories simultaneously.
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Abstract
Sentiment lexicons and datasets represent the knowledge base that lies at the foundation of a SA system. In its simplest form, a sentiment lexicon is a repository of words/phrases labelled with sentiment. Similarly, a sentiment-annotated dataset consists of documents (tweets, sentences or longer documents) labelled with one or more sentiment labels. This chapter explores the philosophy, execution and utility of popular sentiment lexicons and datasets. We describe different labelling schemes that may be used. We then provide a detailed description of existing sentiment and emotion lexicons, and the trends underlying research in lexicon generation. This is followed by a survey of sentiment-annotated datasets and the nuances of labelling involved. We then show how lexicons and datasets created for one language can be transferred to a new language. Finally, we place these sentiment resources in the perspective of their classic applications to sentiment analysis.
Keywords
Sentiment lexicons Sentiment datasets Evaluation Transfer learning
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