Detecting sentiment orientation using supervised learning

Opinion mining is one of the important tasks of natural language processing. Sentiment analysis classify the data into summarization and opinions about the product. The proposed system is based on phrase-level to examine customer reviews. Proposed system extract the features from online reviews and before extracting review it apply pre processing step to each individual sentence of review. This system extract the implicit and explicit features of review. It uses the Apriori algorithm for extracting frequent features. Supervised Naive Bayes determine orientation of extracted aspect Orientation of product review is identified by natural language processing

Revisiting the Transit Timing and Atmosphere Characterization of the Neptune-mass Planet HAT-P-26 b

We present the transit timing variation (TTV) and planetary atmosphere analysis of the Neptune-mass planet HAT-P-26~b. We present a new set of 13 transit light curves from optical ground-based observations and combine them with light curves from the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST), Transiting Exoplanet Survey Satellite (TESS), and previously published ground-based data. We refine the planetary parameters of HAT-P-26 b and undertake a TTV analysis using 33 transits obtained over seven years. The TTV analysis shows an amplitude signal of 1.98 $\pm$ 0.05 minutes, which could result from the presence of an additional $0.02 M_{Jup}$ planet at the 1:2 mean-motion resonance orbit. Using a combination of transit depths spanning optical to near-infrared wavelengths, we find that the atmosphere of HAT-P-26 b contains $2.4^{+2.9}_{-1.6}$% of H$_2$O with a derived temperature of $590^{+60}_{-50}$ K.Comment: 34 pages, accepted by A