Sentiment analysis is a computational analysis of unstructured textual data, used to assess the person's attitude from a piece of text. Aspect-based sentimental analysis defines the relationship among opinion targets of a document and the polarity values corresponding to them. Since aspects are often implicit, it is an extremely challenging task to spot them and calculate their respective polarity. In recent years, several methods, strategies and improvements have been suggested to address these problems at various levels, including corpus or lexicon-based approaches, term frequency and reverse document frequency approaches. These strategies are quite effective when aspects are correlated with predefined groups and may struggle when low-frequency aspects are involved. In terms of accuracy, heuristic approaches are stronger than frequency and lexicon based approaches, however, they consume time due to different combinations of features. This article presents an effective method to analyze the sentiments by integrating three operations: (a) Mining semantic features (b) Transformation of extracted corpus using Word2vec (c) Implementation of CNN for the mining of opinion. The hyperparameters of CNN are tuned with Genetic Algorithm (GA). Experimental results revealed that the proposed technique gave better results than the state-of-the-art techniques with 95.5% accuracy rate, 94.3% precision rate, 91.1% recall and 96.0% f-measure rate.INDEX TERMS Aspect-based sentiment analysis, convolutional neural network, genetic algorithm.

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This paper covers successful Reaming with Casing job in Maino-17 located in Sindh, Pakistan. Throughout previous jobs during drilling in Miano block, Operator observed hole packoff and continuous caving, which raised concerns about running casing to TD. Specifically, drilling 12 ¼" hole section in Sui Main Limestone and Ghazij Shale, pressure and mechanical caving were observed throughout till TD. During wiper trip prior to run in hole 9–5/8" casing the hole condition was not good as it was packing off. Based on the challenges faced in Miano, Reaming with Casing technology was selected. The objective was to run and ream 9 5/8" casing to a total depth (TD) of 5,581 ft (1,701 m) in unstable hole conditions using Reaming with Casing. The difference in pressures between the limestone and the shale increased the possibility of hole caving during casing running. Operator’s team deployed the technology with internal catch tool to run and ream the casing through the pressurized formation. The Reaming with Casing technology provided remotely operated casing running, which eliminated the need for a stabber in the derrick, reduced the number of floor hands, integrated self-interlock system and reduced the risk of casing drop. While running in, the mud weight was 9.7ppg (1,162 kg/m3), which was kept to the minimum to avoid hydrostatic burden on loss prone formations, encountered an obstruction at 5,213 ft (1,589 m) and could not pass through. Using the Reaming with Casing, operator reamed the last ten joints of casing to casing shoe depth at 5,590 ft (1,701 m). The job was completed in approximately 12 hours without incurring any nonproductive time. Based on results, Reaming with Casing Technology was found very efficient, reducing approximately 50% of non-productive time, risk of POOH of whole casing string and associated cost as compare to conventional 9–5/8" casing job bedsides drill string/casing stuck in unstable hole. Using the Reaming with Casing technology enabled OMV to run the casing string to TD and improve operational efficiency in a challenging shale formation and enhanced safety by eliminating or removing personnel from the rig floor.