Multi-class sentiment analysis using a hierarchical logistic model tree approach


  • Masun Nabhan Homsi Universidad Simón Bolívar


This paper proposes a new hybrid system for multi-class sentiment analysis based on General Inquirer (GI) dictionary and a hierarchical Logistic Model Tree (LMT) approach. This new system consists of three layers, the Bipolar Layer (BL) is of one LMT (LMT-1) for classifying sentiment polarity, while the Intensity Layer (IL) comprises two LTMs (LMT-2 and LMT3) for detecting separately three positive and three negative sentiment intensities. Only in construction phase, the Grouping Layer (GL) is used to cluster positive and negative instances by employing 2 k-means respectively. In Pre-processing phase, the raw text data is subjected to a tokenizer, a tagger, a stemmer and finally to GI dictionary to count and label only verbs, nouns, adjectives and adverbs with 24 markers that are used later to compute feature vectors. In Sentiments Classification phase, feature vectors are first introduced to LMT-1, then they are grouped in GL according to class label, afterward these groups of instances are labeled manually, and finally positive instances are introduced to LMT-2 and negative instances to LMT-3. The three trees are trained and tested on Movie Review and SenTube datasets utilizing 10-folds stratified cross validation. LMT-1 yields a tree of 48 leaves and 95 of size with 90.88% of accuracy, while both LMT-2 and LMT-3 provide two trees of 1 leaf and 1 of size with 99.28% and 99.37% of accuracy respectively. Experiments show that the proposed hierarchical classification methodology gives a better performance compared to other prevailing approaches.
Keywords: Multi-class sentiments analysis, hybrid approach, logistic model tree, general inquirer dictionary (GI).

En este trabajo se propone un nuevo sistema híbrido para el análisis de sentimientos en clase múltiple basado en el uso del diccionario General Inquirer (GI) y un enfoque jerárquico del clasificador Logistic Model Tree (LMT). Este nuevo sistema se compone de tres capas, la capa bipolar (BL) que consta de un LMT (LMT-1) para la clasificación de la polaridad de sentimientos, mientras que la segunda capa es la capa de la Intensidad (IL) y comprende dos LMTs (LMT-2 y LMT3) para detectar por separado tres intensidades de sentimientos positivos y tres intensidades de sentimientos negativos. Sólo en la fase de construcción, la capa de Agrupación (GL) se utiliza para agrupar las instancias positivas y negativas mediante el empleo de 2 k-means, respectivamente. En la fase de Pre-procesamiento, los textos son segmentados por palabras que son etiquetadas, reducidas a sus raíces y sometidas finalmente al diccionario GI con el objetivo de contar y etiquetar sólo los verbos, los sustantivos, los adjetivos y los adverbios con 24 marcadores que se utilizan luego para calcular los vectores de características. En la fase de Clasificación de Sentimientos, los vectores de características se introducen primero al LMT-1, a continuación, se agrupan en GL según la etiqueta de clase, después se etiquetan estos grupos de forma manual, y finalmente las instancias positivas son introducidas a LMT-2 y las instancias negativas a LMT-3. Los tres árboles están entrenados y evaluados usando las bases de datos Movie Review y SenTube con validación cruzada estratificada de 10-pliegues. LMT-1 produce un árbol de 48 hojas y 95 de tamaño, con 90,88% de exactitud, mientras que tanto LMT-2 y LMT-3 proporcionan dos árboles de una hoja y uno de tamaño, con 99,28% y 99,37% de exactitud, respectivamente. Los experimentos muestran que la metodología de clasificación jerárquica propuesta da un mejor rendimiento en comparación con otros enfoques prevalecientes.

Palabras clave: Análisis de sentimientos en clase múltiple, enfoque híbrido, logistic model tree, diccionario general inquirer (GI).


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Cómo citar

Nabhan Homsi, M. (2016). Multi-class sentiment analysis using a hierarchical logistic model tree approach. Maskana, 5(Ed. Esp.). Recuperado a partir de