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Register a new userIdentifying Helpful Sentences in Product Reviews
تحديد الجمل المفيدة في مراجعات المنتج
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في السنوات الأخيرة، اكتسب التسوق عبر الإنترنت زخما وأصبح مكانا مهما للعملاء الذين يرغبون في توفير الوقت وتبسيط عملية التسوق الخاصة بهم. إن ميزة أساسية للتسوق عبر الإنترنت هي القدرة على قراءة ما يقوله العملاء الآخرون حول منتجات الاهتمام. في هذا العمل، نهدف إلى الحفاظ على هذه الميزة في المواقف التي يحتاج فيها الإيجاز الشديد، على سبيل المثال، عند التسوق عن طريق الصوت. نقترح مهمة جديدة لاستخراج جملة مفيدة لممثل واحد من مجموعة من الاستعراضات لمنتج معين. يجب أن تلبي الجملة المحددة شرطين: أولا، يجب أن تكون مفيدة لقرار الشراء والثاني، يجب دعم الرأي الذي يعبر عنه من قبل مراجعات متعددة. ترتبط هذه المهمة ارتباطا وثيقا بمهمة تلخيص المستندات المتعددة في نطاق مراجعات المنتج ولكنها تختلف في هدفها ومستوى موجزه. نقوم بجمع مجموعة بيانات باللغة الإنجليزية من الجملة، سهولة الوصول إلى النتائج عبر مصادر الجماهير وإظهار موثوقيتها على الرغم من النشاطية المتأصلة المعنية. بعد ذلك، وصفنا نموذجا كاملا يمسكت جمل مفيدة للممثل مع مشاعر إيجابية وسالبة تجاه المنتج وإظهار أنه يتفوق على العديد من خطوط الأساس.
In recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about products of interest. In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. We suggest a novel task of extracting a single representative helpful sentence from a set of reviews for a given product. The selected sentence should meet two conditions: first, it should be helpful for a purchase decision and second, the opinion it expresses should be supported by multiple reviewers. This task is closely related to the task of Multi Document Summarization in the product reviews domain but differs in its objective and its level of conciseness. We collect a dataset in English of sentence helpfulness scores via crowd-sourcing and demonstrate its reliability despite the inherent subjectivity involved. Next, we describe a complete model that extracts representative helpful sentences with positive and negative sentiment towards the product and demonstrate that it outperforms several baselines.
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With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user's experience of
the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text.
Abstract Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts of text can be problematic, as key pieces may not be explici
t in a local window. We isolate and define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. We describe an annotation procedure, collect data on the Wikipedia corpus, and use the data to train models to automatically decontextualize sentences. We present preliminary studies that show the value of sentence decontextualization in a user-facing task, and as preprocessing for systems that perform document understanding. We argue that decontextualization is an important subtask in many downstream applications, and that the definitions and resources provided can benefit tasks that operate on sentences that occur in a richer context.
For many NLP applications of online reviews, comparison of two opinion-bearing sentences is key. We argue that, while general purpose text similarity metrics have been applied for this purpose, there has been limited exploration of their applicabilit
y to opinion texts. We address this gap in the literature, studying: (1) how humans judge the similarity of pairs of opinion-bearing sentences; and, (2) the degree to which existing text similarity metrics, particularly embedding-based ones, correspond to human judgments. We crowdsourced annotations for opinion sentence pairs and our main findings are: (1) annotators tend to agree on whether or not opinion sentences are similar or different; and (2) embedding-based metrics capture human judgments of opinion similarity'' but not opinion difference''. Based on our analysis, we identify areas where the current metrics should be improved. We further propose to learn a similarity metric for opinion similarity via fine-tuning the Sentence-BERT sentence-embedding network based on review text and weak supervision by review ratings. Experiments show that our learned metric outperforms existing text similarity metrics and especially show significantly higher correlations with human annotations for differing opinions.
A random sample of the kinds of bread consumed in the coastal region during the
years of research was taken, the percentage of fiber and protein was calculated, and the
effect of the mixture approved on an annual basis in mills on purveyance flour
content of
fiber and protein was studied.
The study showed the important role of the mixture, where the percentage of fiber
increased from (1.06%) in 2009 to (1.61%) in 2010, and the percentage of protein
increased also from (11.36%) in 2010 to (13.90%) in 2012. The results show that there are
some, but not all, governmental mills which add soft bran fiber and protein-rich flour,
taking into consideration the impact of technological processes applied throughout the
stages of manufacturing bread, and in particular the stages of fermentation and broil.
The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTM). Although these approaches have achieved significant results, sur
prisingly very little work has been done on how to disentangle the latent topics. Existing topic models when applied to reviews may extract topics associated with writers' subjective opinions mixed with those related to factual descriptions such as plot summaries in movie and book reviews. It is thus desirable to automatically separate opinion topics from plot/neutral ones enabling a better interpretability. In this paper, we propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. We conduct an extensive experimental assessment introducing a new collection of movie and book reviews paired with their plots, namely MOBO dataset, showing an improved coherence and variety of topics, a consistent disentanglement rate, and sentiment classification performance superior to other supervised topic models.
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