This paper describes our submission to SemEval 2014 Task 41 (aspect based sentiment analysis). The current work is based on the assumption that it could be advantageous to connect the subtasks into one workflow, not necessarily following their given order. We took part in all four subtasks (aspect term extraction, aspect term polarity, aspect category detection, aspect category polarity), using...

The article describes our system submitted to the SemEval-2014 task on Aspect-Based Sentiment Analysis. The methods based on distributed representations of words for the aspect term extraction and aspect term polarity detection tasks are presented. The methods for the aspect category detection and category polarity detection tasks are presented as well. Well-known skip-gram model for constructi...

As the internet and its applications are growing, E-commerce has become one of its rapid applications. Customers of E-commerce were provided with the opportunity to express their opinion about the product on the web as a text in the form of reviews. In the previous studies, mere founding sentiment from reviews was not helpful to get the exact opinion of the review. In this paper, we have used A...

In this paper, the UNITOR system participating in the SemEval-2014 Aspect Based Sentiment Analysis competition is presented. The task is tackled exploiting Kernel Methods within the Support Vector Machine framework. The Aspect Term Extraction is modeled as a sequential tagging task, tackled through SVMhmm. The Aspect Term Polarity, Aspect Category and Aspect Category Polarity detection are tack...

This study investigated the acquisition of grammatical aspect by Iranian EFL learners. The main question addressed in this study was whether Iranian EFL learners could distinguish conditions under which they must use either the perfective aspect  or imperfective aspect. A group of 110 adult, Persian-Speaking learners of English both at BA and MA levels Took a Michigan test based on which they w...

In this paper, we develop a novel approach to aspect term extraction based on unsupervised learning of distributed representations of words and dependency paths. The basic idea is to connect two words (w1 and w2) with the dependency path (r) between them in the embedding space. Specifically, our method optimizes the objective w1 + r ≈ w2 in the low-dimensional space, where the multihop dependen...

This paper describes the systems submitted by the University of San Francisco (USF) to Semeval-2014 Task 4, Aspect Based Sentiment Analysis (ABSA), which provides labeled data in two domains, laptops and restaurants. For the constrained condition of both the aspect term extraction and aspect term polarity tasks, we take a supervised machine learning approach using a combination of lexical, synt...

Traditional retrieval models assume that query terms are independent and rank documents primarily based on various term weighting strategies including TF-IDF and document length normalization. However, query terms are related, and groups of semantically related query terms may form query aspects. Intuitively, the relations among query terms could be utilized to identify hidden query aspects and...

Nowadays, when an individual wants to buy any product or a company wants to take the pulse of public opinion about its product, user reviews of this product have become a valuable source of information. As a consequence of that, aspect based sentiment analysis has become popular research field which has also attracted the attention of researchers. In this study, we devised a method which extrac...