Sentiment Analysis for Reputation Management: Mining the Greek Web

Petasis, Georgios; Spiliotopoulos, Dimitris; Tsirakis, Nikos; Tsantilas, Panayiotis

doi:10.1007/978-3-319-07064-3_26

Cited by 20 publications

(20 citation statements)

References 8 publications

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“…A commercial software for polarity detection for entities and sentiment analysis, called "OpinionBuster", is presented in [39]. They presented a name entity recognition module that has been trained to locate entities from the reputation management domain, such as political parties, products of particular vendors and their competitors and perform sentiment analysis using Hidden Markov Models (HMM).…”

Section: Sentiment Analysis For Greek Textsmentioning

confidence: 99%

A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek

Athanasiou

Μaragoudakis

2017

Algorithms

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Sentiment analysis has played a primary role in text classification. It is an undoubted fact that some years ago, textual information was spreading in manageable rates; however, nowadays, such information has overcome even the most ambiguous expectations and constantly grows within seconds. It is therefore quite complex to cope with the vast amount of textual data particularly if we also take the incremental production speed into account. Social media, e-commerce, news articles, comments and opinions are broadcasted on a daily basis. A rational solution, in order to handle the abundance of data, would be to build automated information processing systems, for analyzing and extracting meaningful patterns from text. The present paper focuses on sentiment analysis applied in Greek texts. Thus far, there is no wide availability of natural language processing tools for Modern Greek. Hence, a thorough analysis of Greek, from the lexical to the syntactical level, is difficult to perform. This paper attempts a different approach, based on the proven capabilities of gradient boosting, a well-known technique for dealing with high-dimensional data. The main rationale is that since English has dominated the area of preprocessing tools and there are also quite reliable translation services, we could exploit them to transform Greek tokens into English, thus assuring the precision of the translation, since the translation of large texts is not always reliable and meaningful. The new feature set of English tokens is augmented with the original set of Greek, consequently producing a high dimensional dataset that poses certain difficulties for any traditional classifier. Accordingly, we apply gradient boosting machines, an ensemble algorithm that can learn with different loss functions providing the ability to work efficiently with high dimensional data. Moreover, for the task at hand, we deal with a class imbalance issues since the distribution of sentiments in real-world applications often displays issues of inequality. For example, in political forums or electronic discussions about immigration or religion, negative comments overwhelm the positive ones. The class imbalance problem was confronted using a hybrid technique that performs a variation of under-sampling the majority class and over-sampling the minority class, respectively. Experimental results, considering different settings, such as translation of tokens against translation of sentences, consideration of limited Greek text preprocessing and omission of the translation phase, demonstrated that the proposed gradient boosting framework can effectively cope with both high-dimensional and imbalanced datasets and performs significantly better than a plethora of traditional machine learning classification approaches in terms of precision and recall measures.

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Section: Sentiment Analysis For Greek Textsmentioning

confidence: 99%

A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek

Athanasiou

Μaragoudakis

2017

Algorithms

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“… Dictionary based techniques. The majority of approaches leverage techniques based on dictionaries [6,19,31,[35][36][37][38][39][40][41][42][43][44][45]. The structure of Wikipedia provides useful features for generating dictionaries: ─ Entity pages: Each page in Wikipedia contains a title (e.g.…”

Section: State Of the Art In Entity Detection (Spotting)mentioning

confidence: 99%

“…The recognition of named entities is an important starting point for many tasks in the area of natural language processing. Named Entity Recognition (NER) refers to methods that identify names of entities such as people, locations, organizations and products [1,2]. It is typically broken down into the two subtasks entity detection (or "spotting") and entity classification.…”

Section: Introductionmentioning

confidence: 99%

Improving Language-Dependent Named Entity Detection

Petz

Wetzlinger

Nedbal

2017

Lecture Notes in Computer Science

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Named Entity Recognition (NER) and Named Entity Linking (NEL) are two research areas that have shown big advancements in recent years. The majority of this research is based on the English language. Hence, some of these improvements are language-dependent and do not necessarily lead to better results when applied to other languages. Therefore, this paper discusses TOMO, an approach to language-aware named entity detection and evaluates it for the German language. This also required the development of a German gold standard dataset, which was based on the English dataset used by the OKE 2016 challenge. An evaluation of the named entity detection task using the web-based platform GERBIL was undertaken and results show that our approach produced higher F1 values than the other annotators did. This indicates that language-dependent features do improve the overall quality of the spotter.

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“…Over recent years, social web text (also known as social text) processing and mining has attracted the focus of the Natural Language Processing (NLP), Machine Learning (ML) and Data Mining research communities. The increasing number of users connecting through social networks and web platforms, such as Facebook and Twitter, as well as numerous Blogs and Wikis, creates continuously a significant volume in written communication through the Web [1][2][3][4][5][6][7]. The amount and quality of information and knowledge extracted from social text has been considered crucial to studying and analyzing public opinion [1,3,5,8,9], as well as linguistic [2,7,[10][11][12][13][14][15] and behavioral [4,6,[16][17][18] patterns.…”

Section: Introductionmentioning

confidence: 99%

“…The increasing number of users connecting through social networks and web platforms, such as Facebook and Twitter, as well as numerous Blogs and Wikis, creates continuously a significant volume in written communication through the Web [1][2][3][4][5][6][7]. The amount and quality of information and knowledge extracted from social text has been considered crucial to studying and analyzing public opinion [1,3,5,8,9], as well as linguistic [2,7,[10][11][12][13][14][15] and behavioral [4,6,[16][17][18] patterns. In its typical form, social text is often short in length, low in readability scores, informal, syntactically unstructured, characterized by great morphological diversity and features of oral speech, misspellings and slang vocabulary, consequently presenting major challenges for NLP and Data Mining tasks [2,4,7,10,11,13,[13][14][15][16]19].…”

Section: Introductionmentioning

confidence: 99%

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Nikiforos

Voutos

Drougani

et al. 2021

Data

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Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.

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Sentiment Analysis for Reputation Management: Mining the Greek Web

Cited by 20 publications

References 8 publications

A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek

A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek

Improving Language-Dependent Named Entity Detection

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

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