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Text, as one of the main communication methods, is frequently used as a data source for natural language processing (NLP) studies. Naturally, our thoughts, expressions, and actions are based on our feelings. Therefore, representing the language better to machines involves the problems of reflecting the actual meaning and also, the detecting emotion of the data source. While representing the textual data, word embeddings (e.g. Word2Vec and GloVe (Global Vectors for Word Representation)) which can extract semantic information are frequently used. However, these models may give unexpected results in sentiment and emotion detection studies because of the limitations of capturing emotive data. Because of occurring frequently in similar contexts, some words carrying opposite emotions may have similar vector representations. Nowadays, enriching the vectors by adding emotion or sentiment data is studied which aim to increase the success in emotion detection or classification tasks. The main purpose is to reorganize the vector space in a way that words having semantically and sentimentally similar in closer locations. In this study, three emotion enrichment models over two semantic embeddings (Word2Vec and GloVe) and a contextual embedding (BERT) (Bidirectional Encoder Representations from Transformers)) are applied to a Turkish dataset. Turkish is an agglutinative language. Thus, it is expected to produce different results in this problem, as it has a different structure from the languages that are frequently studied in this field. Besides, experiments on in-category/opposite-category cosine similarity based on eight emotion categories and classification with sequential minimal optimization, logistic regression and multi-layer perceptron are conducted. According to experimental results emotionally enriched vector representations outperform the original models and give promising results. [ABSTRACT FROM AUTHOR]

In multiword expression (MWE) recognition, there exist many studies where different learning methods are employed to decide whether given word combination is a multiword expression. The recognition methods commonly utilize a number of features that are extracted from a data source, frequently from the given text. Though the recognition methods and the features are well studied, we believe that to achieve the best possible performance with a learning method, different subsets of features should also be considered and the best performing subset must be selected. In this paper, we propose a procedure that covers the performance comparison of well-known feature selection methods to obtain the best feature subset in MWE recognition. The evaluation tests are performed on a Turkish MWE data set and the performance is measured by precision, recall and F1 values. The highest F1 value =0.731 is obtained by C4.5 classifier employing either wrapper or filtering method in feature selection. In the regarding setting(s), it is examined that the performance is increased by 1.11% compared to the setting where all features are employed in classification. Based on the experimental results, it may be stated that feature selection improves the performance of MWE recognition by eliminating the noisy/non-effective features. Moreover, it is obvious that proposed feature selection method contributes to the overall MWE recognition system by reducing the measurement and storage requirements due to the lower number of features in classification, providing a faster and more-cost effective learning model. [ABSTRACT FROM AUTHOR]

As a primary means of communication, texts are used to implicitly or explicitly reflect emotions. Emotion or sentiment detection from text has emerged as an important and expanding research area to more clearly understand the actual feelings of humans. Most of the word representation models, such as Word2Vec or GloVe, project the words in vector space such that if words have similar context, then their representations are also very similar. However, according to the recent studies, this approach limits the success of studies in areas such as emotion detection. For instance, love and happy are emotionally similar words, but they may have a lower similarity score than emotionally dissimilar word such as happy and sad which have high co-occurrence frequency, as they are in similar contexts. Recently, researchers propose some methods based on the addition of emotional or sentimental information to the original word vectors. These have improved the vector representation of words and achieved better results in emotion detection or classification tasks. In this survey, we analyze in detail such recent text-based studies in the literature. We summarize their methods used, emotion models, data sources, findings, and performances. • Commonly utilized datasets for text based emotion detection studies are summarized. • Sentiment and emotion detection lexicons are given in comparative manner. • Enriched vector representation based on emotion/sentiment information is discussed. [ABSTRACT FROM AUTHOR]