A Survey on Feature Extraction Techniques, Classification Methods and Applications of Sentiment Analysis

Abstract Rapid developments in the era of IoT technologies, coupled with the espousal of social media tools and applications, have promoted the use of data analytics as a means to gain significant insights from unstructured data. Sentiment analysis is an approach that identifies data polarity to classify a text as positive, neutral, or negative. Also referred to as opinion mining or subjective mining, sentiment analysis has applications that range from marketing and customer service to clinical medicine. The application of sentiment analysis in the epoch of big data has proved invaluable in classifying sentiment and, in general, determining opinions from the average person’s frame of mind Several sentiment analysis techniques have been developed over the years. In this regard, this article presents a brief survey on the sentiment analysis applications, as well as feature extraction and sentiment classification techniques. This article surveys various feature extractions techniques and concludes that each technique has its own pros and cons, and can be combined for better results. The survey on classification methods suggests that hybrid methods provide finer results than individual ones. The survey of applications surmises that sentiment analysis as applied to different sectors, helps expand business opportunities. Also, the paper presents a few open challenges in carrying out sentiment analysis

Hemipteran Fauna (Insecta) Infesting Sandal Santalum Album Linn. in Southern India

Volume: 105Start Page: 223End Page: 22

A new genus and species of Pteromalidae (Hymenoptera: Chalcidoidea) parasitic on Ghoon borers of Bamboo in Karnataka (India)

Cyrtophagoides ghooubori Narcndran gen. & sp. nov. is described as a parasitoid of the Ghoon borer, Dinoderus sp., of Bamboo in Karnataka, India and compared to related genera. Heydonia indica Narcndran (Pteromalidae) is newly reported from the tunnels of Dinoderus sp. and Dendrocalamus strictus (Rosch,) (Pteromalidae) is newly reported from India

Genome-Wide Identification, Characterization and Expression Analysis of Plant Nuclear Factor (NF-Y) Gene Family Transcription Factors in <i>Saccharum</i> spp.

Plant nuclear factor (NF-Y) is a transcriptional activating factor composed of three subfamilies: NF-YA, NF-YB, and NF-YC. These transcriptional factors are reported to function as activators, suppressors, and regulators under different developmental and stress conditions in plants. However, there is a lack of systematic research on the NF-Y gene subfamily in sugarcane. In this study, 51 NF-Y genes (ShNF-Y), composed of 9 NF-YA, 18 NF-YB, and 24 NF-YC genes, were identified in sugarcane (Saccharum spp.). Chromosomal distribution analysis of ShNF-Ys in a Saccharum hybrid located the NF-Y genes on all 10 chromosomes. Multiple sequence alignment (MSA) of ShNF-Y proteins revealed conservation of core functional domains. Sixteen orthologous gene pairs were identified between sugarcane and sorghum. Phylogenetic analysis of NF-Y subunits of sugarcane, sorghum, and Arabidopsis showed that ShNF-YA subunits were equidistant while ShNF-YB and ShNF-YC subunits clustered distinctly, forming closely related and divergent groups. Expression profiling under drought treatment showed that NF-Y gene members were involved in drought tolerance in a Saccharum hybrid and its drought-tolerant wild relative, Erianthus arundinaceus. ShNF-YA5 and ShNF-YB2 genes had significantly higher expression in the root and leaf tissues of both plant species. Similarly, ShNF-YC9 had elevated expression in the leaf and root of E. arundinaceus and in the leaf of a Saccharum hybrid. These results provide valuable genetic resources for further sugarcane crop improvement programs

Preparation of novel compounds, characterization and studying experimentally and theoretically as inhibitors through thermodynamic and quantum chemistry

To inhibit corrosion of the mild steel Q235 type in cooling water systems, two heterocyclic compounds were used, namely (3-(2-hydroxy-3-methoxyphenyl)-5-(4-nitrophenyl)-2-(4-((4-nitrophenyl)diazennyl)phenyl)dihydro-2H-pyrrolo[3,4-d]isoxazole-4,6(5H,6aH)-dione) (A1), and (5-(4-(1,3,5-dithiazinan-5-yl)phenyl)-5-pentyl-1,3,5-dithiazinan-5-ium (A2). They were experimentally evaluated by weight loss method at deference concentrations from 1×10-1 M to 1×10-5 M at 5 hours, and theoretically through thermodynamic functions, such as activation energy, standard free energy of adsorption, enthalpy of adsorption and entropy of adsorption. On the other hand, they were theoretically studied through quantum chemistry, such as quantum parameters including Highest occupied molecular orbital )HOMO( energy, Lowest unoccupied molecular orbital (LUMO) energy, energy gap, dipole moment, chemical potential, ΔEBack-donation, global hardness, global softness, global electrophilicity index, ionization potential, electro negativity and number of transferred electrons. The temperature effect on the corrosion rate has been studied at 25, 35, 45, 55 and 65 °C, and the adsorption for studied inhibitors on mild steel surface obeyed Langmuir adsorption isotherm. The methods of compounds preparation A1 and A2 are different from each other, A1 was prepared through several steps, and A2 through the domino reaction (by two step). The results indicate that the studied inhibitors exhibit good performance as an inhibitors for mild steel corrosion in cooling water systems, and inhibition efficiency increasing with increase inhibitors concentration and decreased with temperature rise