AROMA

Citations Over TimeTop 10% of 2017 papers

Abstract

While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing natural language processing (NLP) methods for Arabic, handling its morphological complexity, and the lack of large-scale opinion resources for Arabic. To close this gap, we examine the class of models used for English and that do not require extensive use of NLP or opinion resources. In particular, we consider the Recursive Auto Encoder (RAE). However, RAE models are not as successful in Arabic as they are in English, due to their limitations in handling the morphological complexity of Arabic, providing a more complete and comprehensive input features for the auto encoder, and performing semantic composition following the natural way constituents are combined to express the overall meaning. In this article, we propose A R ecursive Deep Learning Model for O pinion M ining in A rabic (AROMA) that addresses these limitations. AROMA was evaluated on three Arabic corpora representing different genres and writing styles. Results show that AROMA achieved significant performance improvements compared to the baseline RAE. It also outperformed several well-known approaches in the literature.

Related Papers

• → The role of collaborative networks in supporting the innovation performances of lagging-behind European regions(2017)242 cited
• Comparative analysis of leading and lagging rural regions in OECD countries in the 1980s and 1990s(2004)
• An Exploration to the Lags of the Policy of Curriculum Reform(2014)
• Acoustical pipe lagging systems design and performance(1998)
• → Comparative analysis of leading and lagging rural regions in OECD countries in the 1980s and 1990s(2004)