Shakeel Ahmad

Plant Growth Promoting Rhizobacteria and Silicon Synergistically Enhance Salinity Tolerance of Mung Bean.

The present study explored the eco-friendly approach of utilizing plant-growth-promoting rhizobacteria (PGPR) inoculation and foliar application of silicon (Si) to improve the physiology, growth, and yield of mung bean under saline conditions. We isolated 18 promising PGPR from natural saline soil in Saudi Arabia, and screened them for plant-growth-promoting activities. Two effective strains were selected from the screening trial, and were identified as Enterobacter cloacae and Bacillus drentensis using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry and 16S rRNA gene sequencing techniques, respectively. Subsequently, in a 2-year mung bean field trial, using a randomized complete block design with a split-split plot arrangement, we evaluated the two PGPR strains and two Si levels (1 and 2 kg ha−1), in comparison with control treatments, under three different saline irrigation conditions (3.12, 5.46, and 7.81 dS m−1). The results indicated that salt stress substantially reduced stomatal conductance, transpiration rate, relative water content (RWC), total chlorophyll content, chlorophyll a, chlorophyll b, carotenoid content, plant height, leaf area, dry biomass, seed yield, and salt tolerance index. The PGPR strains and Si levels independently improved all the aforementioned parameters. Furthermore, the combined application of the B. drentensis strain with 2 kg Si ha−1 resulted in the greatest enhancement of mung bean physiology, growth, and yield. Overall, the results of this study provide important information for the benefit of the agricultural industry.

A Unified Framework for Creating Domain Dependent Polarity Lexicons from User Generated Reviews

The exponential increase in the explosion of Web-based user generated reviews has resulted in the emergence of Opinion Mining (OM) applications for analyzing the users’ opinions toward products, services, and policies. The polarity lexicons often play a pivotal role in the OM, indicating the positivity and negativity of a term along with the numeric score. However, the commonly available domain independent lexicons are not an optimal choice for all of the domains within the OM applications. The aforementioned is due to the fact that the polarity of a term changes from one domain to other and such lexicons do not contain the correct polarity of a term for every domain. In this work, we focus on the problem of adapting a domain dependent polarity lexicon from set of labeled user reviews and domain independent lexicon to propose a unified learning framework based on the information theory concepts that can assign the terms with correct polarity (+ive, -ive) scores. The benchmarking on three datasets (car, hotel, and drug reviews) shows that our approach improves the performance of the polarity classification by achieving higher accuracy. Moreover, using the derived domain dependent lexicon changed the polarity of terms, and the experimental results show that our approach is more effective than the base line methods.

SentiHealth: creating health-related sentiment lexicon using hybrid approach

The exponential increase in the health-related online reviews has played a pivotal role in the development of sentiment analysis systems for extracting and analyzing user-generated health reviews about a drug or medication. The existing general purpose opinion lexicons, such as SentiWordNet has a limited coverage of health-related terms, creating problems for the development of health-based sentiment analysis applications. In this work, we present a hybrid approach to create health-related domain specific lexicon for the efficient classification and scoring of health-related users’ sentiments. The proposed approach is based on the bootstrapping modal, a dataset of health reviews, and corpus-based sentiment detection and scoring. In each of the iteration, vocabulary of the lexicon is updated automatically from an initial seed cache, irrelevant words are filtered, words are declared as medical or non-medical entries, and finally sentiment class and score is assigned to each of the word. The results obtained demonstrate the efficacy of the proposed technique.

Bacterial impregnation of mineral fertilizers improves yield and nutrient use efficiency of wheat

BACKGROUNDnThe fertilizer use efficiency (FUE) of agricultural crops is generally low, which results in poor crop yields and low economic benefits to farmers. Among the various approaches used to enhance FUE, impregnation of mineral fertilizers with plant growth-promoting bacteria (PGPB) is attracting worldwide attention. The present study was aimed to improve growth, yield and nutrient use efficiency of wheat by bacterially impregnated mineral fertilizers.nnnRESULTSnResults of the pot study revealed that impregnation of diammonium phosphate (DAP) and urea with PGPB was helpful in enhancing the growth, yield, photosynthetic rate, nitrogen use efficiency (NUE) and phosphorus use efficiency (PUE) of wheat. However, the plants treated with F8 type DAP and urea, prepared by coating a slurry of PGPB (Bacillus sp. strain KAP6) and compost on DAP and urea granules at the rate of 2.0u2009g 100u2009g-1 fertilizer, produced better results than other fertilizer treatments. In this treatment, growth parameters including plant height, root length, straw yield and root biomass significantly (P ≤ 0.05) increased from 58.8 to 70.0u2009cm, 41.2 to 50.0u2009cm, 19.6 to 24.2u2009g per pot and 1.8 to 2.2u2009g per pot, respectively. The same treatment improved grain yield of wheat by 20% compared to unimpregnated DAP and urea (F0). Likewise, the maximum increase in photosynthetic rate, grain NP content, grain NP uptake, NUE and PUE of wheat were also recorded with F8 treatment.nnnCONCLUSIONnThe results suggest that the application of bacterially impregnated DAP and urea is highly effective for improving growth, yield and FUE of wheat.

Aspect-based opinion mining framework using heuristic patterns

The aspect-based online opinions expressed by users on social media sites have become a popular source of information for consumers regarding their purchase decisions as well as for companies seeking opinions on their products. Therefore, it is important to develop aspect-based opinion mining applications with an emphasis on extracting and classifying the aspect-based opinions expressed by users about products in a given review. Previous studies have used a limited set of heuristic patterns for aspect extraction with both supervised (annotated-dataset-based) and unsupervised (lexical-resource-based) aspect-related sentiment classification algorithms. However, the present study proposes an integrated framework comprising of an extended set of heuristic patterns for aspect extraction, a hybrid sentiment classification module with the additional support of intensifiers and negations, and a summary generator. The performance evaluation of the proposed aspect-based opinion mining system using state-of-the-art methods shows that the proposed system outperforms the alternative methods in terms of better precision, recall and F-measure, since it achieves an average precision of 85%, an average recall of 73% and an average F-measure of 0.78. The comparative results indicate that the proposed technique provides more efficient results for the aspect-sentiment extraction, classification and summary generation of online product reviews.

T-SAF: Twitter sentiment analysis framework using a hybrid classification scheme

Of the many social media sites available, users prefer microblogging services such as Twitter to learn about product services, social events, and political trends. Twitter is considered an important source of information in sentiment analysis applications. Supervised and unsupervised machine learning-based techniques for Twitter data analysis have been investigated in the last few years, often resulting in an incorrect classification of sentiments. In this paper, we focus on these issues and present a unified framework for classifying tweets using a hybrid classification scheme. The proposed method aims at improving the performance of Twitter-based sentiment analysis systems by incorporating 4 classifiers: (a) a slang classifier, (b) an emoticon classifier, (c) the SentiWordNet classifier, and (d) an improved domain-specific classifier. After applying the preprocessing steps, the input text is passed through the emoticon and slang classifiers. In the next stage, SentiWordNet-based and domain-specific classifiers are applied to classify the text more accurately. Finally, sentiment classification is performed at sentence and document levels. The findings revealed that the proposed method overcomes the limitations of previous methods by considering slang, emoticons, and domain-specific terms.