Abstract-Stemming algorithms are employed in information retrieval (IR) to reduce verity variants of the same word with several endings to a standard stem. Stemmers can also help IR systems by unifying vocabulary, reducing term variants, reducing storage space, and increasing the likelihood of matching documents, all of which make stemming very attractive for use in IR. This paper aims to study the impact of using stemming techniques in mobile effectiveness. Two-word extraction stemming techniques will be used: a light stemmer and a dictionary-lookup stemmer. Also, three sets of experiments were conducted in this research in order to raise the efficiency of mobile aapplications. Implementing the two stemming approaches and assessing their accuracy by calculating the precision, recall, MAP, and f-measure, produced results which show that the light10 stemmer outperforms the dictionarylookup stemmer in precision and MAP. Furthermore, the mobile performance of the light10 stemmer exceeds that of the dictionary-based stemmer.

Sentiment analysis is consider with extracting specific subjective information from reliable amounts of text. Sentiment analysis holder recognition is approach that has not been considered yet in Arabic Language. This approach essentially requires deep understanding of sentences structures. This paper presents survey for the sentiment analysis operations, classifications and summarization. And proposed sentiment holder Algorithm for extract the holder in Arabic sentences.

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Abstract-Most of the information retrieval (IR) models rank the documents by computing a score using only the lexicographical query terms or frequency information of the query terms in the document. These models have a limitation as they does not consider the terms proximity in the document or the term-mismatch or both of the two. The terms proximity information is an important factor that determines the relatedness of the document to the query. The ranking functions of the Spectral-Based Information Retrieval Model (SBIRM) consider the query terms frequency and proximity in the document by comparing the signals of the query terms in the spectral domain instead of the spatial domain using Discrete Wavelet Transform (DWT). The query expansion (QE) approaches are used to overcome the word-mismatch problem by adding terms to query, which have related meaning with the query. The QE approaches are divided to statistical approach Kullback-Leibler divergence (KLD) and semantic approach P-WNET that uses WordNet. These approaches enhance the performance. Based on the foregoing considerations, the objective of this research is to build an efficient QESBIRM that combines QE and proximity SBIRM by implementing the SBIRM using the DWT and KLD or P-WNET. The experiments conducted to test and evaluate the QESBIRM using Text Retrieval Conference (TREC) dataset. The result shows that the SBIRM with the KLD or P-WNET model outperform the SBIRM model in precision (P@), R-precision, Geometric Mean Average Precision (GMAP) and Mean Average Precision (MAP).

Sentiment analysis is the study of people's opinions, attitudes, feelings, and emotions discuss any object such as entities, events, topics, product, issues, services, etc. are respected for extraction of useful subjective information out of the text. The task is challenging and considered for customers and producers, for instance, Customers need to have general ideas about products, and companies need to know customers' needs and the opportunity to investigate and analyze those opinions towards their products and services. The increasing growth of the social network sites generating a big resource of social data. So, researchers considering about harnessing the effeteness of social media and sentiment analysis. These recent studies classified according to their contributions in the different SA methods. The main target of this paper is to give the illustration of the current trend of research in the sentiment analysis Detection, Recognition, Aspect Identification.