PHYTOCHEMICALS AND ANTIOXIDANT ACTIVITY OF CLERODENDRUM PANICULATUM (L.) LEAF AND FLOWER EXTRACTS

Objective: This study was designed to evaluate the phytochemicals present in the flower and leaf extracts of Clerodendrum paniculatum L., collected from Nelji village of Kodagu district . Methods: The healthy leaves and flowers of C. paniculatum were collected and the plant extracts were prepared using ethanol, hexane and distilled water separately. Phytochemical analysis was conducted using standard procedurs for the flower and leaf extracts of C. paniculatum. The antioxidant activity in leaf and flower extracts was determined by three assays, estimation of total phenolic content, reducing power assay and radical scavenging activity (DPPH) using standard procedures. Results: Phytochemical screening conducted for the flower and leaf extracts of C. paniculatum showed the presence of three phytochemicals, namely saponins, alkaloids and terpenoids. Terpenoids were commonly present in all the extracts of flower and leaf that is in both polar (aqueous and ethanol) solvent and in non-polar (hexane) solvents. The extracts tested for the antioxidant activity showed the presence of total phenolics in ethanol, aqueous and hexane extracts. The aqueous extract showed high redox potential followed by ethanol and hexane extracts. The aqueous leaf extract showed high radical scavenging activity when compared to the flower extracts of C. paniculatum. Conclusion:  The present study showes C. paniculatum to be an important medicinal plant, since the flower and leaves showed good antioxidant activity. Thus it may used in the treatment of diseases and may also used in the preparation of natural or herbal drugs due to the presence of antioxidants

HMDSAD: Hindi multi-domain sentiment aware dictionary

Sentiment Analysis is a fast growing sub area of Natural Language Processing which extracts user's opinion and classify it according to its polarity into positive, negative or neutral classes. This task of classification is required for many purposes like opinion mining, opinion summarization, contextual advertising and market analysis but it is domain dependent. The words used to convey sentiments in one domain is different from the words used to express sentiments in other domain and it is a costly task to annotate the corpora in every possible domain of interest before training the classifier for the classification. We are making an attempt to solve this problem by creating a sentiment aware dictionary using multiple domain data. The source domain data is labeled into positive and negative classes at the document level and the target domain data is unlabeled. The dictionary is created using both source and target domain data. The words used to express positive or negative sentiments in labeled data has relatedness weights assigned to it which signifies its co-occurrence frequency with the words expressing the similar sentiments in target domain. This work is carried out in Hindi, the official language of India. The web pages in Hindi language is booming very quickly after the introduction of UTF-8 encoding style. The dictionary can be used to classify the unlabeled data in the target domain by training a classifier