Survey on Aspect-Level Sentiment Analysis

Abstract

The field of sentiment analysis, in which sentiment is gathered, analyzed, and aggregated from text, has seen a lot of attention in the last few years. The corresponding growth of the field has resulted in the emergence of various subareas, each addressing a different level of analysis or research question. This survey focuses on aspect-level sentiment analysis, where the goal is to find and aggregate sentiment on entities mentioned within documents or aspects of them. An in-depth overview of the current state-of-the-art is given, showing the tremendous progress that has already been made in finding both the target, which can be an entity as such, or some aspect of it, and the corresponding sentiment. Aspect-level sentiment analysis yields very finegrained sentiment information which can be useful for applications in various domains. Current solutions are categorized based on whether they provide a method for aspect detection, sentiment analysis, or both. Furthermore, a breakdown based on the type of algorithm used is provided. For each discussed study, the reported performance is included. To facilitate the quantitative evaluation of the various proposed methods, a call is made for the standardization of the evaluation methodology that includes the use of shared data sets. Semantically-rich concept-centric aspect-level sentiment analysis is discussed and identified as one of the most promising future research direction.with the traditional surveys and questionnaires that often reluctant participants had to fill without any personal motivation to do so, resulting in sub-optimal information.Many individuals are influenced by the opinionated materials they find on the Web. This is especially true for product reviews, which have been shown to influence buying behavior [1]. Moreover, information provided by individuals on the Web is regarded as more trustworthy than information provided by the vendor [1]. From a producers point of view, every person is a potential customer. Hence, knowing their likes and dislikes can be of great help in developing new products [2], as well as managing and improving existing ones [3]. Furthermore, understanding how the information in, for example, product reviews interacts with the information provided by companies enables the latter to take advantage of these reviews and improve sales [4]. In fact, opinions on the Web have become a resource to be harnessed by companies, just like the traditional word-of-mouth [5]. In addition to this traditional producer/consumer model, sentiment analysis is also important for other economic areas, like for example financial markets [6]. DefinitionsThis survey will start with a quick summary of the definitions for aspect-level sentiment analysis set forth by Pang and Lee [3]. The field of sentiment analysis operates at the intersection of information retrieval, natural language processing, and artificial intelligence. This has led to the use of different terms for similar concepts. A term often used is 'opinion 1041-4347 (c)

A hybrid approach to the sentiment analysis problem at the sentence level

“…The authors focus the discussion on methods and lexical resources that aid in extracting sentiment indicators from natural languages in general. A comprehensive work on semantic analysis is Cambria et al [8], while Schouten and Frasincar work [56] provides a complete survey specific to aspect-level sentiment analysis.…”

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

“…Although there are some researches on the choice of rules, it still cannot meet the needs of practical applications. A set of strict extraction rules is leading to high precision but low recall, while a general set of extraction rules is leading to high recall but low precision [11]. Thus, the results with high recall cannot be used directly as final features and sentiment words, and some additional methods need to be used to filter candidate features and sentiment words.…”

“…Frequency-based method generally does not consider the low-frequency features. Together with that, it always takes all the frequent nouns as features [11]. However, for instance, the nouns "wife" and "gift" usually appearing in the reviews are all not features.…”

Sentiment Analysis Tools and Techniques: A Comprehensive Survey

“…An opinion from a single person is usually insufficient and hence a summary of opinions is desirable. This kind of a summary can be provided based on aspects, hence called aspect or feature level sentiment analysis [2]. Document and Sentence level sentiment analysis can determine the overall sentiment towards a service but might not be able to capture the full essence of the review.…”