1. A method for providing augmented product specifications based on user reviews, comprising: obtaining input data of specifications and user reviews on a plurality of products, each product corresponding to a plurality of specifications and a plurality of user reviews, each specification including at least a pair of a feature and a feature-value of the product; concatenating the user reviews of the products to form product-documents, each product-document corresponding to the concatenated user reviews of a product; employing a topic model to process the input data and learn topic distributions in the product-documents and word distributions in topics, the topics including specifications of the products, the topic model being a type of statistical model for discovering topics that occur in a collection of product-documents, each product-document containing one or more topics, and each topic existing in one or more documents, the topics including specification-topics and review-topics, comprising: obtaining prior knowledge including known topic distributions in the product-documents and known word distributions in the topics, and obtaining a type of each word, wherein the type of the word identifies whether the word is a product-specific word or a specification word, and the prior knowledge of known topic distributions in the product-documents further includes known specification-topic distributions in the product-documents and known review-topic distribution in the product-documents separately; incorporating the prior knowledge and employing the topic model to process the input data and learn the topic distributions in the product-documents and the word distributions in the topics; and obtaining an outcome from the topic model, the outcome including learned topic distributions in the product-documents and learned word distributions in the topics; and providing augmented specifications to a user based on the topic model, the augmented specifications including one or more of relevant sentences of the feature-value, feature importance information, and product-specific words of the product, wherein providing augmented specifications based on the topic model further comprises: when the user hovers a cursor on a product feature-value, displaying the relevant sentences in a float box according to a position of the cursor; ranking and displaying the feature importance information in a separate column corresponding to the product features; and displaying the product-specific words in a separate row, wherein the font size and color of the word corresponds to how specific the word is to the product; and wherein, provided that, the prior knowledge about a distribution of a specific word w in a feature topic f is denoted as p(w|f), the known word distributions in the feature-topics follows Zipf's law distribution; a new prior p′(w|f) for each word w is defined as: p ′ ⁡ ( w ❘ f ) = { p ⁡ ( w ❘ f ) Σ w ∈ v ⁡ ( f ) ⁢ p ⁡ ( w ❘ f ) ⁢ ∑ i = 1  v ⁡ ( f ) ⋂ V  ⁢ Zipf ⁡ ( i ) if ⁢ ⁢ w ∈ v ⁡ ( f ) Zipf ⁡ ( rank f ⁡ ( w ) ) otherwise ⁢ wherein v(f) is a vocabulary in f, V is a vocabulary in all reviews, rank f (w) is w's rank in p(w|f) excluding words in v(f), and Zipf's law distribution function Zipf(i) is defined as Zipf ⁡ ( i ) = 1 / i s ∑ n = 1  V  ⁢ ⁢ 1 / n s wherein s is a parameter characterizing the distribution.
1. A method for providing augmented product specifications based on user reviews, comprising: obtaining input data of specifications and user reviews on a plurality of products, each product corresponding to a plurality of specifications and a plurality of user reviews, each specification including at least a pair of a feature and a feature-value of the product; concatenating the user reviews of the products to form product-documents, each product-document corresponding to the concatenated user reviews of a product; employing a topic model to process the input data and learn topic distributions in the product-documents and word distributions in topics, the topics including specifications of the products, the topic model being a type of statistical model for discovering topics that occur in a collection of product-documents, each product-document containing one or more topics, and each topic existing in one or more documents, the topics including specification-topics and review-topics, comprising: obtaining prior knowledge including known topic distributions in the product-documents and known word distributions in the topics, and obtaining a type of each word, wherein the type of the word identifies whether the word is a product-specific word or a specification word, and the prior knowledge of known topic distributions in the product-documents further includes known specification-topic distributions in the product-documents and known review-topic distribution in the product-documents separately; incorporating the prior knowledge and employing the topic model to process the input data and learn the topic distributions in the product-documents and the word distributions in the topics; and obtaining an outcome from the topic model, the outcome including learned topic distributions in the product-documents and learned word distributions in the topics; and providing augmented specifications to a user based on the topic model, the augmented specifications including one or more of relevant sentences of the feature-value, feature importance information, and product-specific words of the product, wherein providing augmented specifications based on the topic model further comprises: when the user hovers a cursor on a product feature-value, displaying the relevant sentences in a float box according to a position of the cursor; ranking and displaying the feature importance information in a separate column corresponding to the product features; and displaying the product-specific words in a separate row, wherein the font size and color of the word corresponds to how specific the word is to the product; and wherein, provided that, the prior knowledge about a distribution of a specific word w in a feature topic f is denoted as p(w|f), the known word distributions in the feature-topics follows Zipf's law distribution; a new prior p′(w|f) for each word w is defined as: p ′ ⁡ ( w ❘ f ) = { p ⁡ ( w ❘ f ) Σ w ∈ v ⁡ ( f ) ⁢ p ⁡ ( w ❘ f ) ⁢ ∑ i = 1  v ⁡ ( f ) ⋂ V  ⁢ Zipf ⁡ ( i ) if ⁢ ⁢ w ∈ v ⁡ ( f ) Zipf ⁡ ( rank f ⁡ ( w ) ) otherwise ⁢ wherein v(f) is a vocabulary in f, V is a vocabulary in all reviews, rank f (w) is w's rank in p(w|f) excluding words in v(f), and Zipf's law distribution function Zipf(i) is defined as Zipf ⁡ ( i ) = 1 / i s ∑ n = 1  V  ⁢ ⁢ 1 / n s wherein s is a parameter characterizing the distribution. 3. The method according to claim 1 , wherein obtaining the prior knowledge further comprises: obtaining a type of each specification word, wherein the type of the specification word identifies whether the word is a feature word or a feature-value word.