Tweet sentiment analysis with classifier ensembles

Silva, Nádia Félix Felipe da; Hruschka, Eduardo R.; Hruschka, Estevam R.

doi:10.1016/j.dss.2014.07.003

Cited by 382 publications

(178 citation statements)

References 14 publications

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“…The proposed approach with h = 4 shows higher accuracies, from 1.3% to 16.4%. Especially, the dataset has a class imbalance problem, the approaches by Saif et al [20] and de Silva et al [21] showed poor performance of recall in the minority class, Positive. However, the proposed approach reports relatively good performances of recall in the minority class, providing more evidence that SIGs are an effective way to train HMMs.…”

Section: Resultsmentioning

confidence: 99%

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Sentiment Analysis in Microblogs Using HMMs with Syntactic and Sentimental Information

Kim¹,

Kim²,

Lee³

2017

IJFIS

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In this paper, we propose an approach for sentiment analysis in microblogs that learns patterns of syntactic and sentimental word transitions. Because sentences are sequences of words, we can more accurately analyze sentiments by properly modeling the sequential patterns of words in sentimental sentences. However, most previous research has focused on just extending feature sets using n-grams, POS tags, polarity lexicons, etc., without considering sequential patterns. Our proposed approach first identifies groups of words that have similar syntactic and sentimental roles, called SIGs (similar syntactic and sentimental information groups). We then build HMMs using the SIGs as hidden states for the initialization. The SIGs function as the prior knowledge of formative elements of sentimental sentences for HMMs. By using the SIGs, HMMs can start with informative hidden states and more precisely model the transition patterns of words in sentimental sentences with robust probability estimation. For the performance evaluation, we compare the proposed approach with existing ones using HCR dataset. The result shows that the proposed approach outperforms the previous ones in various performance measures.

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Section: Resultsmentioning

confidence: 99%

“…A large amount of work has been conducted in this field [1][2][3][14][15][16][17][18][19][20][21][22]. Earlier studies mainly focused on lexical resources.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis in Microblogs Using HMMs with Syntactic and Sentimental Information

Kim¹,

Kim²,

Lee³

2017

IJFIS

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“…Da Silva vd. MNB, DVM, RO ve Lojistik Regresyon (LR) algoritmalarını, önermiş oldukları ensemble yöntemi ile birleştirerek beş farklı veri seti üzerinde %76,84 ile %87,20 arasında başarı oranı elde etmişlerdir [14]. Çatal ve Nangir NB ve DVM algoritmalarını çeşitli ensemble yöntemleri ile birleştirerek %86,13'e varan başarı oranı elde etmişlerdir [15].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Sentiment Analizinde Öznitelik Düşürme Yöntemlerinin Oto Kodlayıcılı Derin Öğrenme Makinaları ile Karşılaştırılması

Kaynar¹,

Aydın²,

Görmez³

2017

Bilişim Teknolojileri Dergisi

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Özet-Günümüz teknolojisinde internetin her kesim tarafından çok yoğun olarak kullanılmasından dolayı insanlar artık görüş, fikir ve hislerini sosyal paylaşım siteleri, forum, blog benzeri birçok ortam aracılığı ile paylaşmaya başlamıştır. Ancak her geçen gün artan veri sayısı ve boyutu, bu verilerden manuel olarak anlamlı bilgiler çıkartılmasını çok zahmetli ve pahalı bir iş haline getirmektedir. Otomatik olarak verinin duygu içerip içermediğinin saptanması ve bu duygunun olumlu, olumsuz veya tarafsız olma durumunun belirlenmesi duygu analizi yardımıyla gerçekleştirilmektedir. Duygu düşünce analizinde, konuşma dilinin karmaşıklığı, değerlendirilen metin sayısının fazlalığı ve uzunluğu, çok sayıda gereksiz ve gürültü içeren öznitelik vektörüne neden olmaktadır. Boyut problemi olarak adlandırılan bu durum hesaplama zamanın artmasına ve sınıflama hatalarına yol açmaktadır. Bu çalışmada ise bahsedilen problemlere çözüm olarak önerilen derin öğrenme tabanlı oto kodlayıcı (Autoencoder) modeli ile gürültü giderici oto kodlayıcı (Denoising Autoencoder) modeli boyut düşürme tekniği olarak kullanılmış ve literatürde yaygın olarak kullanılan diğer boyut düşürme teknikleri ile kıyaslanmıştır. Elde edilen tüm veri setleri için sınıflama algoritması olarak Destek Vektör Makinaları ve Yapay Sinir Ağları kullanan farklı modeller geliştirilmiştir. Yapılan analizlerin sonucunda, boyut düşürme tekniklerinin duygu analizi için elde edilen sonuçları iyileştirdiği, önerilen oto kodlayıcı modellerinin ise var olan tekniklere benzer ya da onlardan daha iyi sonuçlar aldığı gözlemlenmiştir.Anahtar Kelimeler-Boyut düşürme, Oto kodlayıcı, Yapay sinir ağları, Destek vektör makineleri, Duygu analizi, Derin öğrenme Comparison of Feature Reduction Methods with Deep Autoencoder Machine Learning in Sentiment AnalysisAbstract-Because the internet is extensively used by people from all strata with today's technology, people now share their opinions, ideas and feelings through a variety of media such as social networking sites, forums and blogs. However, the number and size of data that is increasing day by day makes it very laborious and expensive to extract meaningful information manually from these data. Determination of whether data includes emotions or not automatically and determination of these feelings being positive, negative and neutral are performed by sentiment analysis. In sentiment analysis, the complexity of the speech language, the excessive number and length of texts being evaluated causes a large number of unnecessary and noise-containing feature vectors. This situation, which is called dimensionality problem, leads to increase of computation time and classification errors. In this study, a deep autoencoder model and a denoising autoencoder model are proposed and used as dimension reduction methods to overcome mentioned problems and compared with other feature reduction methods commonly used in literature. For all data sets obtained, different models have been developed using Support Vector Machines and Artificial Neural Networks ...

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“…Hashing has proven to be simple, efficient and effective. It has been applied to various tasks including protein sequence classification (Caragea et al, 2012), sentiment analysis (Da Silva et al, 2014), and malware detection (Jang et al, 2011).…”

Section: Feature Hashingmentioning

confidence: 99%

Feature Hashing for Language and Dialect Identification

Malmasi

Dras²

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 2: Short Papers)

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We evaluate feature hashing for language identification (LID), a method not previously used for this task. Using a standard dataset, we first show that while feature performance is high, LID data is highly dimensional and mostly sparse (>99.5%) as it includes large vocabularies for many languages; memory requirements grow as languages are added. Next we apply hashing using various hash sizes, demonstrating that there is no performance loss with dimensionality reductions of up to 86%. We also show that using an ensemble of low-dimension hash-based classifiers further boosts performance. Feature hashing is highly useful for LID and holds great promise for future work in this area.

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Tweet sentiment analysis with classifier ensembles

Cited by 382 publications

References 14 publications

Sentiment Analysis in Microblogs Using HMMs with Syntactic and Sentimental Information

Sentiment Analysis in Microblogs Using HMMs with Syntactic and Sentimental Information

Sentiment Analizinde Öznitelik Düşürme Yöntemlerinin Oto Kodlayıcılı Derin Öğrenme Makinaları ile Karşılaştırılması

Feature Hashing for Language and Dialect Identification

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