9,781 research outputs found
Twitter analysis for depression on social networks based on sentiment and stress
Detecting words that express negativity in a social media message is one step towards detecting depressive moods. To understand if a Twitter user could exhibit depression over a period of time, we applied techniques in stages to discover words that are negative in expression. Existing methods either use a single step or a data subset, whereas we applied a multi-step approach which allowed us to identify potential users and then discover the words that expressed negativity by these users. We address some Twitter specific characteristics in our research. One of which is that Twitter data can be very large, hence our desire to be able to process the data efficiently. The other is that due to its enforced character limitation, the style of writing makes interpreting and obtaining the semantic meaning of the words more challenging. Results show that the sentiment of these words can be obtained and scored efficiently as the computation on these dataset were narrowed to only these selected users. We also obtained the stress scores which correlated well with negative sentiment expressed in the content. This work shows that by first identifying users and then using methods to discover words can be a very effective technique
M3PS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization in E-commerce
Given the long textual product information and the product image, Multi-Modal
Product Summarization (MMPS) aims to attract customers' interest and increase
their desire to purchase by highlighting product characteristics with a short
textual summary. Existing MMPS methods have achieved promising performance.
Nevertheless, there still exist several problems: 1) lack end-to-end product
summarization, 2) lack multi-grained multi-modal modeling, and 3) lack
multi-modal attribute modeling. To address these issues, we propose an
end-to-end multi-grained multi-modal attribute-aware product summarization
method (M3PS) for generating high-quality product summaries in e-commerce. M3PS
jointly models product attributes and generates product summaries. Meanwhile,
we design several multi-grained multi-modal tasks to better guide the
multi-modal learning of M3PS. Furthermore, we model product attributes based on
both text and image modalities so that multi-modal product characteristics can
be manifested in the generated summaries. Extensive experiments on a real
large-scale Chinese e-commence dataset demonstrate that our model outperforms
state-of-the-art product summarization methods w.r.t. several summarization
metrics
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