## 2 Materials and Methods
### Natural Language Processing  
Natural Language Processing (NLP) belongs to the combination of artificial intelligence and linguistics. It mainly uses mathematical models and algorithms to allow computers to recognize and understand human language. Early natural language processing technology is based on statistical concepts to train models. Converting a large amount of text into a dictionary-like format allows the computer to calculate the probability of encountering words and sentences. Recently, NLP has been widely combined with deep learning. The most famous one is BERT, which is a model developed by google based on Transformer [5].  
Sentiment analysis (SA) is the use of NLP, text analysis and computational linguistics to systematically recognize personal opinions and emotional conditions. A common application of which is to collect the voice of customers for marketing purpose, such as product reviews and survey. [PERSON] presented a ranking product model based on sentiment analysis combined with an intuitionistic fuzzy TODIM method. Using the online review data to help customers make purchasing decisions, the proposed method considers  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Trend Lines**} \\ \cline{2-5}  & **Numbering** & **Trend Name** & **Numbering** & **Trend Name** \\ \hline \multirow{10}{*}{**Interface-related**} & I14 & Mono-Bi-Poly(Similar)-Interface & I23 & Market Evolution \\ \cline{2-5}  & I15 & Mono-Bi-Poly(Various)-Interface & I24 & Design Point \\ \cline{2-5}  & I16 & Mono-Bi-Poly(Inc. diff.)-Interface & I25 & Degrees of Freedom \\ \cline{2-5}  & I17 & Nesting-Up & I26 & Boundary Breakdown-interface \\ \cline{2-5}  & I18 & Reduced Damping & I27 & Trimming \\ \cline{2-5}  & I19 & Senses Interaction & I28 & Controllability \\ \cline{2-5}  & I20 & Color Interaction & I29 & Human Involvement \\ \cline{2-5}  & I21 & Transparency & I30 & Design Methodology \\ \cline{2-5}  & I22 & Customer Purchase Focus & I31 & Reducing Energy Conversions \\ \hline \multirow{3}{*}{**Time-related**} & T32 & Action Co-ordination & T35 & Mono-Bi-Poly(Similar-Time) \\ \cline{2-5}  & T33 & Rhythm Co-ordination & T36 & Mono-Bi-Poly(Various-Time) \\ \cline{2-5}  & T34 & Non-linearity & T37 & Macro to Nano Scale-Time \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: The _Space Segmentation_ trend (S2) and its evolving benefits.  
consumers' subjective needs and different sentiment orientations (positive, neutral, and negative) for each product feature [6]. Based on BERT fine-tuning, [PERSON] et al. [7] converted \"Aspect-Based Sentiment Analysis (ABSA)\" from a single sentence classification task to a sentence-pair classification task by constructing auxiliary sentences, so that the final recommendation effect is greater than the classification effect using SA only.  
[PERSON] and [PERSON] used statistical methods to construct a computer-aided effect recognition system to optimize the quality of the recommendation system to match the specific topic. Combined with the accumulation of the knowledge of many experts, the system can put forward the priority of problem solving according to the principle of \"similar problems have similar solutions\", and achieve higher efficiency for the solution recommendation system [8]. However, the texts used in the contents of a patent are more technology-oriented than sentiment-oriented. Regarding patent recommendation for product design, SAO structure analysis combined with dependency syntax analysis is more suitably adopted as described below.